A taxonomy for sentiment analysis field

Abstract

Purpose Due to the large and fast growing sentiment analysis (SA) area recently, many new concepts and different nomenclatures have emerged without the desired organization. This confusion in the research field makes the understandability of the concepts hard and also hampers the comparison of different approaches. Thus, this paper aims to propose a hierarchical taxonomy to help the consolidation of SA area. The taxonomy aims at covering the addressed problems and methods in the SA field. Design/methodology/approach This taxonomy is a filtered union of various classifications found in the literature with a proposal of nomenclatures standardization. As instance, a case study is presented with 20 SA-related articles classified according to the proposed taxonomy. Findings This taxonomy is very expressive because it covers many concepts and is also effective once it allows the distinction and categorization of the previous SA works. Originality/value To the best of the authors’ knowledge, the literature does not present such an expressive and effective classification as proposed in this paper. This new taxonomy allows for the navigation between the existing concepts in the SA field as well as, facilitates the search, comparison and indexing of papers already published.

Evaluation & Analysis of Movie Aspects: Based on Sentiment Analysis

“…Categories of Sentiment Analysis (Jindal & Aron, 2021) The first method is to process text data based on various machine learning algorithms, which can be further broken down into supervised and unsupervised learning techniques (Jindal & Aron, 2021). Basically, machine learning methods extract keywords in text which would be regarded as features to learn and analyze (Rodrigues et al, 2018). The lexicon-based method assumes that synonyms have the same sentiment polarity and antonyms have opposite memory, and the text is analyzed by creating a universal dictionary (Birjali et al, 2021).…”