What is Sentiment Analysis

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Introduction

Sentiment Analysis is an important process in Artificial Intelligence (AI) and Natural Language Processing (NLP) which analyses sentiments, opinions, and attitudes that people express through writing or speaking. By examining how people communicate, machines will be able to determine what people think and how they feel about different subjects, products, services, and experiences.

AI Sentiment Analysis results are typically classified by positive, negative, or neutral; although sophisticated systems will allow for classification of more subtle emotional indicators, such as satisfaction or frustration, excitement or anger.[1]

How Sentiment Analysis Works

Step 1: Text Preprocessing

Deleting miscellaneous information from the raw data removes noises, such as punctuation, stop words, special characters, or anything irrelevant so the accuracy increases considerably.

Step 2: Tokenization

To analyse the language structure more easily, the cleaned text is split into smaller segments (tokenized) with respect to what is being tokenized (word, phrase, etc.).

Step 3: Feature Extraction

Identifying keywords, phrases, signal words (context), as well as linguistic patterns used to express sentiment are all converted into usable features.

Step 4: Modelling

Sentiment is analysed using:

  • A variety of rule-based systems (lexicons and pre-defined rules) exist.
  • Traditional ML Models.
  • Advanced DL Models (for example, transformers [or] LLMs).

Step 5: Classification

A model will assign a sentiment label (or score), e.g., positive, negative, neutral to quantify opinions mining on a large scale (quantifying opinion).[2]

Fig 1 shows a sentiment analysis of restaurant customers across various service and experience categories.

Benefits of Sentiment Analysis

Transforms Unstructured Data

Transforms unstructured text data into useful information.

Improves Decision-Making

Makes it possible to make informed business decisions based on customer feedback.

Enhances Customer Experience

Reveals feelings of customers to better connect with them.

Real-Time Monitoring

Monitor public feelings on various platforms, including social media, reviews, and product feedback.

Supports Business Strategy

Provides insight for better business planning.

Scalable Emotion Analysis

Brings awareness to the emotions of consumers on a large scale for customer-centric business growth.[3]

Types of Sentiment Analysis

  • Document-level Sentiment Analysis: It’s an overall analysis of the sentiment for an entire document (e.g., a review or report) including an evaluation of sentiment expressed by individual sentences.
  • Sentence-level Sentiment Analysis: Judges the sentiment expressed in specific sentences.
  • Aspect-based Sentiment Analysis: Identify sentiments regarding specific characteristics/features of the product (e.g., “Pricing,” “Customer Service,” or “Performance”).[4]

Challenges in Sentiment Analysis

  • Language Complexity: Many people find it hard to tell when someone is being sarcastic or ironic when they use slang words.
  • Context Dependency: Different contexts give the same word multiple meanings.
  • Domain Sensitivity: Industry leaders have predicted that generic pattern models will not cover their needs.
  • Multilingual Limitations: Accuracy is affected by cultural differences and language differences.
  • Data Quality Issues: Unclean/bias data results in fewer trustworthy outcomes.
  • Limited Emotional Depth: Standard custom patterns generated through traditional means result in limited analysis options (i.e., Only providing positive, negative and neutral assessments).

Bring structure and meaning to large datasets using StatsWork’s AI and ML expertise.

References

  1. Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2(1), 325-347.https://www.annualreviews.org/content/journals/10.1146/annurev-linguistics-011415-040518
  2. Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (Eds.). (2017). A practical guide to sentiment analysis(Vol. 5). Cham: Springer International Publishing. https://link.springer.com/book/10.1007/978-3-319-55394-8
  3. Schouten, K., & Frasincar, F. (2015). The benefit of concept-based features for sentiment analysis. In Semantic Web Evaluation Challenges(pp. 223-233). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-319-25518-7_19
  4. Qazi, A., Raj, R. G., Hardaker, G., & Standing, C. (2017). A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges. Internet Research, 27(3), 608-630. https://www.emerald.com/intr/article-abstract/27/3/608/185844/A-systematic-literature-review-on-opinion-types?redirectedFrom=fulltext
  5. Mohammad, S. M. (2017). Challenges in sentiment analysis. In A practical guide to sentiment analysis(pp. 61-83). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-319-55394-8_4