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What Is Sentiment Analysis: Definition & Glossary

A sentiment analysis platform helps you track and measure sentiment in data such as survey responses, news, voice-of-the-customer data, reviews, etc for customer feedback analysis and brand insights. This article gives you a brief overview of this machine-learning technique for intelligence gathering and a list of common terms related to sentiment analysis.

What Is Sentiment Analysis?

Sentiment analysis is the automated process of mining emotion from data in order to tell you if the sentiment expressed in it is negative, positive, or neutral. Sentiment analysis uses many machine learning tasks such as natural language processing (NLP), named entity recognition (NER), semantic clustering, neural networks, and others.

Using sentiment analysis you can identify sentiment in any type of data such as customer experience, employee experience, brand experience, news and public opinion, social media listening, etc.

Read in detail about what is sentiment and its benefits.

Sentiment Analysis Steps - An Overview

Extracting sentiment from data is not an easy task. However, if the machine learning model, whatever form it may be in - API or platform - is trained properly and its tasks such as natural language processing (NLP), named entity recognition (NER) etc., are robust, the model can overcome many issues that arise in sentiment analysis during data mining.

Read about the challenges of sentiment analysis.

The entire sentiment analysis process can be broadly divided into four steps as described below.

Step 1. Data Collection

Data is collected either directly using the URL of the online source, for example, the YouTube video link for YouTube comments analysis, or manually uploading the data, which could be social media comments, reviews, or survey responses, as an excel file.

Step 2. Data Processing

The data is cleaned and prepared for text analysis using natural language processing (NLP) algorithms and semantic clustering. The NLP tasks identify, isolate and categorize aspects, entities, and topics. Multilingual data analysis scans the data for different languages to add to the text pipeline.

Step 3. Sentiment Analysis

Every topic, aspect, and entity is analyzed for sentiment and scored between -1 to +1, with 0 being neutral. This gives you aspect-based sentiment scores as well as an aggregate brand sentiment score.

Step 4. Insight Visualization

All the sentiment insights are presented in the form of charts, graphs, and word clouds.

Read in detail how sentiment analysis is done.

Sentiment Analysis Glossary

As we decode what is sentiment analysis, there are several terms one comes across that are crucial to the understanding of this important ML technique. Below is a glossary of key terminologies that are part of sentiment analysis.

1. Machine Learning

Machine learning (ML) is a branch of artificial intelligence (AI) that enabled the automated understanding of data and data patterns to extract meaningful insights for various industry applications.

2. Machine Learning model

The ML solution that is used to analyze sentiment and text from data. It can be in the form of an API or as a software platform with a user interface that includes a visual dashboard.

3. API

Application programming interface (API) or a medium for two or more computer programs or algorithms to communicate with each other in order to share information. Know more.

4. API Call

An message sent by an API to a server, such as to a social media website, requesting information.

5. Deep Learning

Deep learning is a sub-task of machine learning that is based on artificial neural networks that mimic the workings of a human brain in understanding patterns in data, memorizing them, and applying them for predictive analysis. Deep Learning can be supervised, semi-supervised or unsupervised. Know more.

6. Knowledge Graphs

Knowledge graphs are interlinked descriptions of entities and their relationships with each other and events. They are crucial to understanding what is sentiment analysis. Know more.

7. Neural Networks

Neural networks are algorithms that mimic how a human brain works in order to recognize relationships between data points. This is how an ML model gets smarter as it processes more and more data.

8. Named Entity Recognition (NER)

The process of identifying words that are related to an entity such as person, place, telephone number, currency, etc. Read more.

9. Natural Language Processing (NLP)

NLP is the computational processing of human languages through machine learning algorithms that analyze text to understand grammar, tonality, sentence structures, etc, to extract essential information. [Read more.]hmed-entity-recognition-api/)

10. Semantic Analysis

Analyzing the context and relationship between words and phrases, in relation to entities identified in the data to understand the intent of the text. Read more.

11. Text Analytics

Text analysis is the machine learning-based process of extracting meaningful insights from unstructured and scattered data. Read more.

12. Topic Clustering

Topic clustering is the process in which an algorithm analyzes a multitude of data and categorizes it by topics and related subtopics it identifies in it.

13. Chunking

The task of identifying and isolating groups of words that form meaningful expressions such as “fast food” or “sour patch” but have different meanings when separated.

14. Lemmatization

The automated process of grouping together inflected forms of a word such as reading, reader, reads - so they can be deconstructed into their root word (read) and analyzed.

15. Tokenization

The ML task of splitting sentences into smaller units to simplify them for text analysis.

16. Multilingual Data Analysis

The ML-driven task of automatically identifying language and analyzing the text to understand what is sentiment analysis based on the context. Read more.

17. Speech Tagger

The task of marking up a word in relation to a particular part of speech, based on its contextual definition.

18. Data sources

A data source is a location from where the data to be analyzed is obtained such as social media websites, surveys, emails, documents, etc.

19. Quantitative data

Any data that can be measured numerically such as the number of participants, number of social media likes, number of content shares, etc.

20. Qualitative data

Data that needs to be analyzed for sentiment as it can’t be measured numerically such as customer opinions.

21. Open-ended responses

Data that does not have any pre-determined answers such as yes or no. Read more.

22. Metadata

Data that gives information about another type of data such as the description of a document or an image.

23. Data Sets

A collection of data items that are related to a particular kind of data such as industry, language, etc, and can be used to train a machine learning platform for sentiment analysis. This article makes it clearer to understand what is sentiment analysis in terms of its dependency on quality data sets.

24. Industry Aspect Model

When the machine learning API or platform is trained in the data sets of their relevant industries. For example, a Banking aspect model is an ML model that is trained specifically on banking training data so that it can identify, categorize, and analyze banking-specific aspects such as deposit, teller, locker, etc.

25. Social Media Networks

Social media networks are another way of saying social media websites or platforms such as Facebook, Instagram, and so on.

26. Voice of the Customer

Customer feedback data that is collected from customers in various forms - surveys, review forums, social media, etc.

27. Customer Experience Insights

Important discoveries from customer feedback data analysis using sentiment analysis and text analytics. Read more.

28. Employee Experience Insights

Crucial findings from employee experience and engagement feedback data that have been analyzed for the sentiment. Read more.

29. Brand Experience Insights

Findings from sentiment analysis of data gathered from various touch points that customers have had in their interactions with a brand.

30. Brand Comparison

The ML-driven method of comparing customer sentiment related to various aspects of a customer’s experience with a brand to establish the superiority of one brand over another. This function is used strategically in advertising campaigns. Read more.

31. Fan experience analysis

Analyzing fan sentiment related to aspects of their experience in stadiums during games and events. Read more.

32. Survey data analysis

A machine learning approach to analyzing survey responses including open-ended responses. Read more.

33. Social Media Listening

Social listening is the process of monitoring and analyzing social media content including user-generated videos and comments for brand mentions and relevant keywords such as from TikTok social listening or Instagram social listening.

34. Social Media Sentiment Analysis

Analyzing sentiment in social media listening data. Read more.

35. Influencer Marketing

What is sentiment analysis in terms of influencer marketing tactics? It may not seem upfront but as popular as influencer marketing is, companies depend on social media sentiment analysis to find out which influencers are best suited to positively influence a brand and sales. Read more.

36. Topic-based Sentiment Analysis

Topic-based sentiment analysis refers to emotion mining based on topics that machine learning algorithms identify and extract from data.

37. Aspect-based Sentiment Analysis

Aspect-based sentiment analysis refers to the process of emotion mining of aspects occurring in topics that are identified by the ML model.

38. Aspect-Emotion Co-occurrence

The task of identifying aspects such as price, food, drink, etc occurring with emotions such as happy, sad, grateful, etc. Read more.

39. Video Content Analysis

Video analysis is the process of analyzing video content by extracting audio, captions, and images in the video to identify sentiment. Read more.

40. Emotion Detection

Identifying negative, positive, or negative emotions in data. Read more.

41. Fuzzy Matching

The task of removing redundancies in data to increase the accuracy of insights by identifying false positives. Read more.

42. Multimodal Sarcasm Detection

The process of analyzing sentiment in data taking into account various elements of textual and non-textual clues including emojis so that there are no false positives or false negatives.

43. False Negative

A negative sentiment that is not truly negative but is ascertained so by the algorithm due to its inability to distinguish sentiment in text.

44. False Positive

A positive sentiment that is not truly positive but is ascertained so by the algorithm due to its inability to distinguish sentiment in text.

45. Sentiment Score

The measurement of sentiment in data after it has been processed through other ML tasks such as NLP, NER, and semantic analysis. This measurement can be topic or aspect-based. The aggregate score is based on the polarity of sentiment in the aspects and topics. Learn more.

46. Polarity

Opinion mining is important but what is sentiment analysis of use if we do not know the intensity of the opinion expressed. Polarity is the measure of this sentiment extracted from a text based on the tone of the emotion - positive, negative, neutral - expressed.

47. Real-time Sentiment Analysis

Sentiment analysis of data as it is being created such as live Twitter feeds or live radio broadcasts. Read more.

Patterns in sentiment over a period of time.

49. Word Cloud

A cluster of important words that the ML model extracts from the data to summarize it based on the frequency of the words. Read more.

50. Visualization Dashboard

The visualization tool on which all the sentiment analysis insights can be seen for further analysis for business and growth strategies. Read more.


Sentiment analysis extracts hidden insights from sources such as customer experience, brand experience, and social media listening data to give you crucial business intelligence. Repustate’s sentiment analysis platform, Repustate IQ, provides high-precision insights for these sources and more seamlessly in 23 languages. Each language is analyzed with its own native multilingual data analysis algorithm.

The solution caters to multiple industries and comes in aspect models specific to your business. These data analysis examples show you how it can extract insights from varied data sources and types from across industries, effortlessly. Also, check out these customer Success Stories to see practical ways in which our clients the world over have leveraged Repustate’s AI solution to boost their performance.

Experience Repustate IQ live to find out more about what is sentiment analysis in terms of review sentiment analysis, employee engagement, news monitoring, and more. The solution is available both as an API and a comprehensive solution with an intelligent dashboard.