Reference | Objectives | Lexicon type | Materials | Output |
---|---|---|---|---|
[35] | To predict whether an online text expresses positive, negative or neutral sentiments without the need for supervision | Dictionary-based approach | The Cornell Movie Review dataset, The Obama-McCain Debate dataset, the SemEval-2015 dataset | Positive, negative or neutral |
[82] | To improve the SWN performance by building a new lexical resource named SentiMI | SentiMI based classification, SentiWordNet | Movie review dataset | Positive, negative and objective |
[85] | To present a web-based system known "TweeSent" that can estimate the polarity and emotion of tweets based on their input data from Twitter | NRC emotion lexicon | Tweets from Twitter | Joy, happiness, sadness, anger, trust, surprise, anticipation, fear, positive and negative |
[87] | To classify movie reviews into positives, negatives and neutral polarity | The lexicon that has been published by Hu and Liu (2004) | Twitter data | Positives, negatives and neutral |
[88] | To improve SentiWordNet performance and propose a complete sentiment analysis and classification framework according to SentiWordNet based vocabulary | SentiWordNet based classification | Large movie review dataset, Cornell movie review dataset, multi-domain sentiment datasets | Positive, negative or neutral |
[89] | To investigate Alaskans’ perceptions and opinions on various energy sources and, in particular, clean energy sources | Subjectivity lexicon of English adjectives called ADJLex | Twitter data (Alaskans’ review) on energy consumption | Positive, neutral and negative |
[80] | To recognise the emotional segmentation of a movie reviewer by extracting the sentiments from a given text and classifying them | Dictionary-based methods | Text movie review (IMDB) | Positive and negative |
[90] | To automatically analyse student feedbacks (known as OMFeedback) | Vader Sentiment Intensity Analyser database of English sentiment words (Vader Lexicon) | Feedback | Positive, negative and neutral |
[91] | To extract and classify sentiments and emotions from 141,208 headlines of global English news sources regarding the coronavirus disease (COVID-19) | NRC emotion lexicon, R package “sentiment” | English Headlines news sources | Positive, negative and neutral |
[92] | To identify the public opinion of Filipino Twitter users concerning COVID-19 in three different timelines | Lexicon-based Approach R package “sentiment dictionary” | Twitter textual (COVID-19) | Positive, negative, joy, sadness, fear, anticipation, anger, trust, surprise, disgust |
[93] | To classify user reviews and use co-occurrence analysis to identify passengers’ concerns on different aspects of service in the aviation industry | Vader and Pattern lexicons | Reviews on SKYTRAX | Positive, negative and neutral |
[94] | To study people’s reactions and emotions regarding Trump’s primary debates | R package “sentiment dictionary” | Tweets regarding the Trump Republican primary debate | Negative or positive |
[95] | To illustrate and analyse the emotional sentiment of the campaign speeches of the two main candidates of 2016 US presidential elections | Word-Emotion Association Lexicon | Text files of American Presidency Project website | Negative and Positive |
[96] | To estimate the reputation polarity of tweets | RepLab 2013 collection | Twitter data in English and Spanish | Positive, negative or neutral |
[83] | To categorise YouTube comments based on content relevance | Wordnet | Keenformatics | Relevant, irrelevant, positive and negative |
[97] | To correlate the distinct twitter comments of statesmen of distinct countries for having concrete knowledge on the application of drugs to patients attacked by COVID-19 | TextBlob lexicon | Positive and negative |