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What is Sentiment Analysis: Definition, Key Types and Algorithms

Posted on by Pavel Tantsiura
In this issue:

Every person has some kind of attitude towards things he experiences. We can like this handwritten notes feature in the smartphone but can’t stand the whole noise meter shebang. And there is also this face-lock thing that really puzzles us. It is a natural thing…

All this says something about an object in question. And since this thing can be used by many people – there are dozens of such opinions from many people. When combined all these opinions paint a distinct picture of how the particular product is perceived.

That’s sentiment analysis in a nutshell.

In this article, we will look at what is sentiment analysis and how it can be used for the benefit of your company.

What is the Sentiment Analysis? Ultimate Definition

Sentiment analysis is one of the Natural Language Processing fields, dedicated to the exploration of subjective opinions or feelings collected from various sources about a particular subject. 

In more strict business terms, it can be summarized as: 

  • Sentiment Analysis is a set of tools to identify and extract opinions and use them for the benefit of the business operation 

Such algorithms dig deep into the text and find the stuff that points out the attitude towards the product in general or its specific element.

In other words, opinion mining and sentiment analysis mean an opportunity to explore the mindset of the audience members and study the state of the product from the opposite point of view. This makes sentiment analysis a great tool for:

  • expanded product analytics
  • market research
  • reputation management
  • precision targeting
  • marketing analysis
  • public relations (PR)
  • product reviews
  • net promoter scoring
  • product feedback
  • customer service

How does Sentiment Analysis work?

Sentiment analysis is a predominantly classification algorithm aimed at finding an opinionated point of view and its disposition and highlighting the information of particular interest in the process.

What is an “opinion” in sentiment analysis? You all know the general definition of opinion: “a view or judgment formed about something, not necessarily based on fact or knowledge.”

Well, from the data science standpoint, an opinion is much more than this:  

  • On the one hand, it is a subjective assessment of something based on personal empirical experience. It is partially rooted in objective facts and partly ruled by emotions.
  • On the other hand, an opinion can be interpreted as a sort of dimension in the data regarding a particular subject. It is a set of signifiers that in combination present a point of view, i.e., aspect for the particular issue. Think about it as if it was one of the rings of Saturn.

With that in mind, Sentiment Analysis is applied for the following operations:

  • Find and extract the opinionated data (aka sentiment data) on a specific platform (customer support, reviews, etc.)
  • Determine its polarity (positive or negative)
  • Define the subject matter (what is being talked about in general and specifically)
  • Identify the opinion holder (on its own and in correlation with the existing audience segments)

Depending on the purpose, sentiment analysis algorithm can be used at the following scopes:

  • Document-level – for the entire text.
  • Sentence-level – obtains the sentiment of a single sentence.
  • Sub-sentence level – obtains the sentiment of sub-expressions within a sentence.

Given its subjective matter, mining an opinion is a tricky affair. Opinions differ. Some are more valuable than others. Four subcategories further characterize an opinion:

  • The direct opinion is the one that directly states something. For example, “the responsiveness of the buttons in application X is poor.” Here you have a legit point.
  • Comparative Opinion is the one where X is compared with Y based on specific criteria. For example, “the responsiveness of the button in application X is worse than in application Y.” In addition to being an insight into your product, it also serves as micro competitive research.
  • The explicit opinion is where everything is clearly defined. For example, “this chair is rocking.”
  • Implicit opinions are implied but not clearly stated. For example, “the app started lagging in two days.” It is important to note that implicit opinions may also have idioms and metaphors, which complicates the sentiment analysis process.

Why Sentiment Analysis Matters?

Sentiment Analysis deals with the perception of the product and understanding of the market through the lens of sentiment data.

There are many sources of public and private information out of which you can harness an insight into the customer’s perception of the product and general market situation. To name a few:

  • Customer support correspondence (regarding your product)
  • User-generated Product reviews
  • Professional product reviews (as in The Verge or Wired)
  • Social Media tractions
  • General and special-purpose forums

Customer Sentiment Analysis can help make sense out of these hoards of data and transform it into:

  • a clearly defined view on what certain segments of the customers think about the product or in general
  • A deep dive into the state of the market from the consumer’s standpoint.

In both cases, it is an influential factor in formulating and elaborating the value proposition for a specific audience segment.

Let’s go back to the beginning of the section and take a closer look at how helps with understanding the market and understanding of the product:

  • As one of the key performance indicators – the right kind of perception is strategically vital for the further evolution of the product. Often, sentiment tracking is a decisive factor in choosing the direction of the marketing efforts and business development, and it is crucial to know for sure what the score is. Sentiment analysis marketing gives you an opportunity to pinpoint the strong and weak points of the product from the consumer’s point of view.
  • In the case of market research, the role of sentiment analysis is less integral but influential nonetheless. It gives another perspective, adds additional colors to the picture of the market, and lets you look at the situation from the ground level. And this lets you find one or two untapped leeways that will help to find a niche and establish the product on the market.

While on the initials stages these activities are relatively easy to handle with basic solutions – at some point, it starts to make sense to use more elaborate tools and extract more sophisticated insights.

Types of Sentiment Analysis

To understand how to apply sentiment analysis in the context of your business operation – you need to understand its different types.

In this section, we will look at the main types of sentiment analysis.

1st type. Fine-grained Sentiment Analysis involves determining the polarity of the opinion. It can be a simple binary positive/negative sentiment differentiation. This type can also go into the higher specification (for example, very positive, positive, neutral, negative, very negative), depending on the use case (for example, as in five-star Amazon reviews).

2nd type. Emotion detection is used to identify signs of specific emotional states presented in the text. Usually, there is a combination of lexicons and machine learning algorithms that determine what is what and why.

3rd type. Aspect-based sentiment analysis goes deeper. Its purpose is to identify an opinion regarding a specific element of the product. For example, the brightness of the flashlight in the smartphone. The aspect-based analysis is commonly used in product analytics to keep an eye on how the product is perceived and what are the strong and weak points from the customer’s point of view.

4th type. Intent Analysis is all about the action. Its purpose is to determine what kind of intention is expressed in the message. It is commonly used in customer support systems to streamline the workflow.

See also: Why Business Applies Sentiment Analysis

Sentiment Analysis Algorithms

There are two major Sentiment Analysis methods. Let’s look at both.

Rule-based approach

Rule-based sentiment analysis is based on an algorithm with a clearly defined description of an opinion to identify. Includes identify subjectivity, polarity, or the subject of opinion.

The rule-based approach involves a basic Natural Language Processing routine. It involves the following operations with the text corpus:

  • Stemming
  • Tokenization
  • Part of speech tagging
  • Parsing
  • Lexicon analysis (depending on the relevant context)

Here’s how it works:

  • There are two lists of words. One of them includes only the positive ones, the other includes negatives.
  • The algorithm goes through the text, finds the words that match the criteria.
  • After that, the algorithm calculates which type of words is more prevalent in the text. If there are more positive words, then the text is deemed to have a positive polarity.

The thing with rule-based algorithms is that while it delivers some sort of results – it lacks flexibility and precision that would make them truly usable. For instance, the rule-based approach doesn’t take the context into account. However, it can be used for general purposes of determining the tone of the messages, which may come in handy for customer support.

These days, rule-based sentiment analysis is commonly used to lay the groundwork for the subsequent implementation and training of the machine learning solution.

Automatic Sentiment Analysis

While the rule-based approach is more of a toy than a real tool, automated sentiment analysis is the real deal. It is the one approach that truly digs into the text and delivers the goods. Instead of clearly defined rules – this type of sentiment analysis uses machine learning to figure out the gist of the message.

Because of that, the precision and accuracy of the operation drastically increase and you can process the information on numerous criteria without getting too complicated.

In essence, the automatic approach involves supervised machine learning classification algorithms. In fact, sentiment analysis is one of the more sophisticated examples of how to use classification to maximum effect. In addition to that, unsupervised machine learning algorithms are used to explore data.

Overall, Sentiment analysis may involve the following types of classification algorithms:

  • Linear Regression
  • Naive Bayes
  • Support Vector Machines
  • RNN derivatives LSTM and GRU.

Sentiment Analysis Challenges

If there is one thing for sure, it is that sentiments are tricky beasts.

On the surface, it seems like a routine extraction of the particular insight. But in reality, the sentiment extraction requires a bit of heavy lifting in order to really get the gist of it.

In this section, we will discuss the most common challenges that occur during the sentiment analysis operation.

Context and Polarity definition

Context is the thing that often stings perfectly fine sentiment mining operation right in the eye. While a human being is able to get the context without much of an effort – things are very different from the algorithm’s perspective.

The thing is – Algorithms can’t guess what they need to do in order to get the right results. They need to be configured to get the right results.

Because of that, the sentiment analysis model must contain an additional component that would tackle the context of the message.

The key is in the text vectorization that maps out the connections of the words in the text and their relations to each other in terms of parts of speech.

This gives an additional dimension to the text sentiment analysis and paves the wave for a proper understanding of the tone and mode of the message. Tools like word2vec and doc2vec can do this with ease.

Subjectivity and Tone determination

The identification of the tone of the message is one of the fundamental features of the sentiment analysis.

Overall, the tonality is relatively easy to calculate out of the message via the verbiage. Words like “nice” and “ugly” directly state the score.

The harder task is to determine whether the message is objective or subjective.

People tend to formulate the message in a variety of ways. Sometimes the message does not contain the explicit sentiment, sometimes the implicit sentiment is not what it seems.

The only solution for that is deeper and more varied verbiage in the NLP sentiment analysis model applied for the sentiment analysis.

You need to take into account various options regarding the characterization of the product and group them into relevant categories. This way, the algorithm would be able to correctly determine subjectivity and its correlation with the tone.

Irony and Sarcasm identification

Among all the things sentiment analysis algorithms have troubles with – determining an irony and sarcasm is probably the most meddlesome.

Why? Let’s call it The Treachery of Language.

You see – the way we use language is often subtly subversive. The words on their own might be a bunch of teddy bears, but the context they are used in can turn them into pink elephants on parade.

The algorithm does not get it. They make jokes and snarks at face value and classifies them as a moderately negative sentiment or an overwhelmingly positive one. Even messier is the differentiation between irony and sarcasm.

The secret of successfully tackling this issue is in deep context analysis and diverse corpus used to train the NLP sentiment analysis model.

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Defining a Neutral Tone

Determining tonality can be hard enough due to contextual peculiarities and irony/sarcasm contamination.

And then there is a neutral tone.

What is a neutral tone? It is a type of tone that doesn’t contain any signifiers that can be classified as either positive or negative. Instead, a neutral message just states some facts.

How to deal with neutral messages? There are two ways.

First, you need to take a look at the context and see which facts are stated. That makes all the difference and takes the lid off the unexpressed opinion. But this approach is manual and can be applied in special cases only.

The second is for the algorithm. Neutral tone can be calculated out of what it is not i.e. polar message. Basically, you tag as neutral everything which cannot be identified as positive, negative, or its variations.

In Conclusion

Sentiment analysis is an incredibly valuable technology for businesses because it allows getting realistic feedback from your customers in an unbiased (or less biassed) way. Done right, it can be a great value-added to your systems, apps, or web projects. 

Why Business Applies Sentiment Analysis? 5 Successful Examples

Posted on by Pavel Tantsiura

 

In this issue:

Business information can be useful in gaining a competitive edge once you start applying the insights to your brand and processes within the company. Sentiment analysis can help get these insights and understand what your customers are looking for in your product.

To apply it correctly, you have to understand what sentiment analysis is used for and how to do sentiment analysis for the benefit of the cause.

We already looked at the sentiment analysis technology in our previous article and this article will focus on the most prominent sentiment analysis examples.

open-ended-questions

Sentiment Analysis Examples

Reputation Management – Social Media Monitoring – Brand Sentiment Analysis

Brand monitoring and reputation management is the most common use of sentiment analysis across different markets. No wonder – understanding how the consumers perceive your brand/product/service is equally useful for tech companies, marketing agencies, fashion brands, media organizations, and many others.

In essence, the sentiment analysis application brings additional flexibility and insight into the presentation of the brand and its products. It allows companies to:

  • track the perception of the brand by the customers;
  • point out the specific details about the attitude;
  • Find patterns and trends;
  • keep a close eye on the presentation by the influencers.

All this allows us to adjust to the state of things accordingly and give the product a proper presentation.

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Overall, sentiment analysis can be used to:

  • Automate media monitoring process and the accompanying alert system
  • Monitor mentions or reviews of the brand on different platforms (blogs, social media, review sites, forums, etc.)
  • Categorize urgency of mentions according to the relevancy scoring (i.e., which platform, type of user is vital to the brand)

The whole operation consists of two stages:

  • At the initial stage, the company reacts to the incoming results and adapts.
  • Over time, sentiment analysis can transform the course of action from reacting to managing the perception.

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Use case: How KFC is doing it

An excellent example of how to use sentiment analysis for brand building and monitoring is KFC. For a while, KFC was stuck in the past, while the competition was moving ahead and reinventing themselves with the narratives of healthy food and feel-good experiences.

So, instead of trying to establish themselves in the crowded niche, KFC had chosen to use the ubiquitous power of the brand. KFC started riding on the waves of memes and pop culture iconography (most recently by using RoboCop to promote the newest product) to instill the brand’s value proposition.

This approach generates natural traction around the brand that is augmented by the pop culture reference. As a result, users engage with the brand and ultimately are led to engage with the product down the line. You have to react and adapt almost instantly, which is where sentiment analysis kicks in.

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The method combines sentiment analysis in social networks monitoring and campaign management that involves:

  • Performance monitoring with aspect-based sentiment analysis to point out the specific elements of the presentation
  • Topic mining to extract new ideas and variations.

This creates a loop that perpetuates the campaign’s proceedings.

Due to the nature of the marketing campaign, the users are actively involved in commenting or reacting to the ad content. In turn, this generates further ideas for the development of the campaign.

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The result:

  • KFC brand is constantly present in the media landscape and that presence guarantees the steady growth of the reach and ultimately the market share.
e-commerce-sentiment-analysis

Market Research, Competitor Analysis

Apart from brand perception and customer opinion exploration, market research is probably the most prominent field of sentiment analysis application.

It is important to note that sentiment analysis is not the primary tool for market research. However, it can bring an additional perspective on the market and give a couple of handy insights about how the state of things is seen from the ground level i.e. consumers.

In addition to that, you can use a similar approach to analyze the competition and their marketing efforts.

Here’s what you can do with sentiment analysis:

  • Gather information across different platforms
  • User-generated content (comments, reviews, etc)
  • News articles
  • Influencer content
  • Competitor’s content
  • Extract numerous insights on different criteria
  • General perception
  • Perception of the specific aspect
  • Provide results in real-time
  • Fill the gaps in business intelligence

Sentiment analysis can elaborate on the needs and demands of the consumers and help to adjust your value proposition so that it would hit all the right marks.

Example: How Apple is doing it

The way Apple presents its products and establishes them on the market is a fine example of sentiment analysis application for the benefit of market research and competitor analysis.

Think about how neatly the product’s strong points fit general pains and disgruntlement of the various segments of the user.

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For example:

  • Bad design – you don’t even need to think when using our stuff.
  • Poor privacy – we keep personal data use at an absolute minimum.
  • Low battery life – we’ve got resource management tools.

Such things can be pointed out by analyzing the competitors and their movements on the market in general by specific aspects. For example:

  • Brand value proposition
  • Addressing various issues
  • Introducing new features
  • Announcing milestones and so on

A combination of this information from several maps out the market situation and allows calculating an additional perspective on how to differentiate and strengthen its value proposition.

The result:

  • Apple is a trillion-dollar company because they listen to the customer.
social-media-platforms

Product Analytics

The use of sentiment analysis in product analytics stems from reputation management. Conceptually, it is very similar to brand monitoring. But instead of brand mentions, it goes for specific comments and remarks regarding the product and its performance in specific areas (user interface, feature performance, etc).

This kind of insight is very important at the initial stages with MVP when you need to try the product by fire (i.e. actual users) and make it as polished as possible.

At this stage, the most basic way to apply sentiment analysis is to gather and categorize feedback for further improvements.

Sentiment analysis algorithm can do the dirty work and show what kind of feedback goes from which segment of the audience and at what it points.

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Usually, the whole thing is divided between the following types:

  • Brand keywords
  • Brand-adjacent keywords
  • Customer needs
  • Customer sentiment
  • Competitors analysis (based on similar criteria)

As a result, this can be a significant factor in the product’s successful establishment on the market.

At the later stages, the use of sentiment analysis in product analytics merges with brand monitoring and provides a multi-dimensional view of the product and its brand:

  • How the brand/product is perceived by various target audience segments?
  • Which elements of the product or its presentation are the points of contention and in what light?

Use case: How Google is doing it

A good showcase of how sentiment analysis application contributes to product improvement can be seen in Google’s output. Let’s take the Chrome browser for example.

Google Chrome’s development team is constantly monitoring user feedback, whether it is direct or indirect (i.e. presented in the open sources, most notably, blogs).

But they are not looking at feedback as a message from the user but rather as a sum of its parts:

  • the sentiment itself (positive or negative)
  • Mentions of the specific aspects of the product – whether it is scalability, extensions, security, or UI
  • Sentiments, wishes, and recommendations regarding the product in general and its specific elements

The result:

  • These elements provide an additional perspective on the weak and strong points of the product
  • This subsequently contributes to further research and development of the product

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Voice of the Customer Analysis

Accurate target audience segmentation and subsequent value proposition formulation are amongst the key elements of effective business operation. You need to know where are you aiming at with what.

On the other side of the spectrum, you have to keep the hand on the pulse of your customer in order to remain relevant and keep your product in demand.

In the very center of both activities is an understanding of the “Voice of the customer”.

However, one does not simply capture and study the voice of the customer. It is scattered around the different platforms and presented in a variety of conflicting forms. All this needs to be sorted out nice and clear.

Customer Sentiment Analysis algorithms are capable of capturing and studying the voice of the client with much bigger accuracy.

The process is twofold.

  • During Market Research – sentiment analysis can be used to explore target audience segments in general. It can help to define and further specify what particular segment wants and needs, expects from such products, which similar products are preferred or in use in the segment, and so on.
  • Regarding the product itself – sentiment analysis can be used to analyze direct and indirect customer feedback from multiple platforms. You can study the experiences customers had with your product and determine what it means for the business.

Example: How TripAdvisor is doing it

A good example of VOC analysis done right is TripAdvisor.

The company applies aspect-based sentiment analysis in order to make the most out of the immense amount of data it generates. The aspect-based approach allows to extracts the viable points regarding customer feedback and the service itself.

As the result, sentiment analysis gives an additional perspective on various parts of the business operation, which allows us to understand what the target audience needs, thinks, feels can be improved, and so on.

Ultimately, this contributes to the further polish of the service and strengthening of customer engagement by providing them with what they need.

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Customer Support – feedback analysis

Customer Support is one of the marquee elements of sentiment analysis application in real life.

There are several ways sentiment analysis can be implemented:

  • Insight into customer’s opinions regarding the product: 

           1) The general perception of the product – whether it is positive or negative;

           2) Aspect-based – regarding specific elements of the product;

           3) Reaction to the Service – whether it is effective or not. May also include more detailed analysis regarding particular aspects such as response time or quality of interaction;

  • Intent Analysis for process automation – so that routine queries will be handled automatic scenarios, such as frequently asked questions and basic product use information.
  • Workflow management and customer prioritization. For example, you have a disgruntled customer – his ticket is prioritized to be processed as soon as possible.

The most prominent example of using sentiment analysis in customer support can be seen in big tech companies.

Just think about how detailed and responsive are the troubleshooting quizzes from Microsoft or Apple products. They are specifically designed to generate as much information from the user as possible.

Its purpose is twofold – it is used to solve an issue and also to give additional insight into the peculiarity of the product use.

As a result, the company can continuously map out the strong and weak points of the product and related services and improve its quality seamlessly.

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Read also: Convolutional Neural Networks Applications

What’s next?

Sentiment Analysis is one of those technologies, the usefulness of which wholly depends on the understanding of its capabilities. 
It can be extremely useful if you know how to use it and it can be completely useless if you apply it on something it is not supposed to do. 
This article gives several examples of how to do sentiment analysis to the maximum effect and get the most of your data for the benefit of your company.

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Natural Language Processing Tools and Libraries

Posted on by Pavel Tantsiura
In this issue:

Natural language processing helps us to understand the text receive valuable insights. NLP tools give us a better understanding of how the language may work in specific situations. Moreover, people also use it for different business purposes. Such proposes might include data analytics, user interface optimization, and value proposition. But, it was not always this way.

The absence of natural language processing tools impeded the development of technologies. In the late 90s, things had changed. Various custom text analytics and generative NLP software began to show their potential.

Now the market is flooded with different natural language processing tools.

Still, with such variety, it is difficult to choose the open-source NLP tool for your future project.

In this article, we will look at the most popular NLP processing tools, their features, and use cases.

Let’s start

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8 Best NLP tools and libraries

natural language processing tools examples NLTK NLP Tool

1. NLTK – entry-level open-source NLP Tool

Natural Language Toolkit (AKA NLTK) is an open-source software powered with Python NLP. From this point, the NLTK library is a standard NLP tool developed for research and education.

NLTK provides users with a basic set of tools for text-related operations. It is a good starting point for beginners in Natural Language Processing.

Natural Language Toolkit features include:

  • Text classification
  • Part-of-speech tagging
  • Entity extraction
  • Tokenization
  • Parsing
  • Stemming
  • Semantic reasoning

NLTK interface includes text corpora and lexical resources.

They include:

  • Penn Treebank Corpus
  • Open Multilingual Wordnet
  • Problem Report Corpus
  • and Lin’s Dependency Thesaurus

Such technology allows extracting many insights, including customer activities, opinions, and feedback.

Natural Language Toolkit is useful for simple text analysis. But, if you need to work on a massive amount of data, try something else. Why? Because in this case, Natural Language Toolkit requires significant resources.

Do you want to know more about the NLTK application?

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Stanford Core NLP Library

2. Stanford Core NLP – Data Analysis, Sentiment Analysis, Conversational UI

We can say that the Stanford NLP library is a multi-purpose tool for text analysis. Like NLTK, Stanford CoreNLP provides many different natural language processing software. But if you need more, you can use custom modules.

The main advantage of Stanford NLP tools is scalability. Unlike NLTK, Stanford Core NLP is a perfect choice for processing large amounts of data and performing complex operations.

With its high scalability, Stanford CoreNLP is an excellent choice for:

  • information scraping from open sources (social media, user-generated reviews)
  • sentiment analysis (social media, customer support)
  • conversational interfaces(chatbots)
  • text processing, and generation(customer support, e-commerce)

This tool can extract all sorts of information. It has smooth named-entity recognition and easy mark up of terms and phrases.

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Apache OpenNLP - Data Analysis and Sentiment Analysis

3. Apache OpenNLP – Data Analysis and Sentiment Analysis

Accessibility is essential when you need a tool for long-term use, which is challenging in the realm of Natural Language Processing open-source tools. Because while being powered with the right features, it could be too complex to use.

Apache OpenNLP is an open-source library for those who prefer practicality and accessibility. Like Stanford CoreNLP, it uses Java NLP libraries with Python decorators.

While NLTK and Stanford CoreNLP are state-of-the-art libraries with tons of additions, OpenNLP is a simple yet useful tool. Besides, you can configure OpenNLP in the way you need and get rid of unnecessary features.

Apache OpenLP is the right choice for:

  • Named Entity Recognition
  • Sentence Detection
  • POS tagging
  • Tokenization

You can use OpenNLP for all sorts of text data analysis and sentiment analysis operations. It is also perfect in preparing text corpora for generators and conversational interfaces.

SpaCy - NLP Library

4. SpaCy – Data Extraction, Data Analysis, Sentiment Analysis, Text Summarization

SpaCy is the next step of the NLTK evolution. NLTK is clumsy and slow when it comes to more complex business applications. At the same time, SpaCy provides users with a smoother, faster, and efficient experience.

SpaCy, an open-source NLP library, is a perfect match for comparing customer profiles, product profiles, or text documents.

SpaCy is good at syntactic analysis, which is handy for aspect-based sentiment analysis and conversational user interface optimization. SpaCy is also an excellent choice for named-entity recognition. You can use SpaCy for business insights and market research.

Another SpaCy advantage is word vector usage. Unlike OpenNLP and CoreNLP, SpaCy works with word2vec and doc2vec.

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Still, the main advantage of SpaCy over the other NLP tools is its API. Unlike Stanford CoreNLP and Apache OpenNLP, SpaCy got all functions combined at once, so you don’t need to select modules on your own. You create your frameworks from ready building blocks.

SpaCy is also useful in deep text analytics and sentiment analysis.

AllenNLP - Text Analysis, Sentiment Analysis

5. AllenNLP – Text Analysis, Sentiment Analysis

Built on PyTorch tools & libraries, AllenNLP is perfect for data research and business applications. It evolves into a full-fledged tool for all sorts of text analysis. This way, it is one of the more advanced Natural Language Processing tools on this list.

AllenNLP uses SpaCy open-source library for data preprocessing while handling the rest processes on its own. The main feature of AllenNLP is that it is simple to use. Unlike other NLP tools that have many modules, AllenNLP makes the natural language process simple. So you never feel lost in the output results. It is an excellent tool for inexperienced users.

The machine comprehension model provides you with resources to make an advanced conversational interface. You can use it for customer support as well as lead generation via website chat.

So, the textual entailment model guarantees smooth and comprehensible text generation. You can use it for both multi-source text summarization and simple user-bot interaction.

The most exciting model of AllenNLP is Event2Mind. With this tool, you can explore user intent and reaction, which are essential for product or service promotion.

Omit, AllenNLP is suitable for both simple and complex tasks. AllenNLP performs specific duties with predicted results and enough space for experiments.

GenSim NLP Library

6. GenSim – Document Analysis, Semantic Search, Data Exploration

Sometimes you need to extract particular information to discover business insights. GenSim is the perfect tool for such things. It is an open-source NLP library designed for document exploration and topic modeling. It would help you to navigate the various databases and documents.

The key GenSim feature is word vectors. It sees the content of the documents as sequences of vectors and clusters. And then, GenSim classifies them.

GenSim is also resource-saving when it comes to dealing with a large amount of data.

The main GenSim use cases are:

  • Data analysis
  • Semantic search applications
  • Text generation applications (chatbot, service customization, text summarization, etc.)
TextBlob Library - Conversational UI, Sentiment Analysis

7. TextBlob Library – Conversational UI, Sentiment Analysis

TextBlob is the fastest natural language processing tool. TextBlob is an open-source NLP tool powered by NLTK. It could be enhanced with extra features for more in-depth text analysis.

You can use TextBlob sentiment analysis for customer engagement via conversational interfaces. Besides, you can build a model with the verbal skills of a broker from Wall Street.

Another TextBlob notable feature is machine translation. Content localization has become trendy and useful. For that, it would be great to have your website/application localized in an automated manner. Using TextBlob, you can optimize the automatic translation using its language text corpora.

TextBlob also provides tools for sentiment analysis, event extraction, and intent analysis features. TextBlob has different flexible models for sentiment analysis. Thus, you can build entire timelines of sentiments and look at things in progress.

 

Intel NLP Architect - Data Exploration, Conversational UI3

8. Intel NLP Architect – Data Exploration, Conversational UI3

Intel NLP Architect is the newer application in this list. Intel NLP Architect uses Python library for deep learning using recurrent neural networks. You can use it for:

  • text generation and summarization
  • aspect-based sentiment analysis
  • and conversational interfaces such as chatbots

One of its most exciting features is Machine Reading Comprehension. NLP Architect applies a multi-layered approach by using many permutations and generated text transfigurations. In other words, it makes the output capable of adapting the style and presentation to the appropriate text state based on the input data. You can use it for more personalized services.

The other great feature of Architect NLP is Term Set Expansion. This set of NLP tools fills in the gap of data based on its semantic features. Let’s look at an example.

When making research on virtual assistants, your initial input would be “Siri” or “Cortana.” Term Set Expansion (TSE) adds the other relevant options as “Amazon Echo.” In more complex cases, TSE is capable of scraping bits and pieces of information based on longer queries.

NLP Architect is the most advanced tool being one step further, getting deeper into the sets of text data for more business insights.

You might also like Guide to machine learning applications: 7 major fields.

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Choosing a Particular NLP Library

Natural Language Processing tools are all about analyzing text data and receiving useful business insights out of it.

But it is hard to find the best NLP library for your future project. This way, to make the right decision, you should be aware of the alternatives. Also, you should choose your next NLP tool according to its use case. There is no reason to take a state-of-the-art library when you need to wrangle the text corpus and clean it from all data noise.

If you want to receive a consultation on Natural Language Processing, fill in the contact form, and we will get in touch.

 

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