Sentiment classification in social media data by combining triplet belief functions

Abstract

Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyse online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organising strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets.

Will sentiment analysis need subculture? A new data augmentation approach

“…Medical and healthcare institutions can monitor patients' mental states, thereby facilitating early intervention, diagnosis, and treatment (Gohil et al, 2018). The analysis of sentiments expressed in public opinion posts, comments and messages also empowers individuals and organizations to understand their reputation, manage financial affairs, track, and predict social events (Bi, 2022; Paltoglou, 2016; Ren et al, 2021; Sinha et al, 2022; Yildirim, 2022). Also, sentiment analysis can assist policymakers and government agencies in shaping political campaigns, strategies, and messaging, as well as developing assessments about public policy and resource allocation (Chung & Zeng, 2016; Verma, 2022).…”

News Classification and Categorization with Smart Function Sentiment Analysis

“…In the context of news classifcation and categorization, a smart function based on sentiment analysis can foster brand trust and elevate a brand's reputation if the sentiment analysis results are positive. Text categorization and classifcation share many similarities, with the latter being a subset of the former [9][10][11]. In text categorization, the frst step is to represent the text by preprocessing the documents and creating a vector space containing the words present in the documents using the bag-of-words (BoW) model.…”

“…Consequently, the classifcation of text documents relies on the proximity of keyword vectors, with the signifcance of keywords in the documents often determined by weighting schemes such as term frequency or word frequency. In contrast, sentiment analysis involves identifying relevant keywords in a textual document using linguistic patterns [11].…”

“…Additionally, the part of speech (POS) technique is employed to tag words with their grammatical attributes, such as nouns, adverbs, verbs, or adjectives. Tese types of representations are commonly used in sentiment classifcation research [9,11,12]. Tis study employed a smart function to obtain unambiguous search results from the BBC search engine and then conducted sentiment analysis to assess the reputation of BBC news.…”

“…In a study conducted by Bi [11], a novel approach was introduced for sentiment analysis that utilized a belief functions framework to merge sentiment analysis outputs. Te study evaluated the efectiveness of sentiment stratifcation individually and in combination.…”