Lexicon-Based Sentiment Analysis for Movie Review Tweets

Azizan, Amizon; Jamal, Nurul Najwa Sk Abdul; Abdullah, Mohammad; Mohamad, Mohd Saberi; Khairudin, Nurkhairizan

doi:10.1109/aidas47888.2019.8970722

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…Such approaches make use of lexicon dictionary that consists of list of positive, negative and neutral words. The work in [22] developed a framework that classifies movie reviews into positives, negatives and neutral polarity using the lexicon published in [23] which has 2195 positive words and 4972 negative words. 100 tweets were used for evaluation.…”

Section: Lexicon-based Approachesmentioning

confidence: 99%

Twitter Sentiment Analysis Approaches: A Survey

Adwan¹,

Al-Tawil²,

Huneiti³

et al. 2020

Int. J. Emerg. Technol. Learn.

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Twitter is one of the most popular microblogging and social networking platforms where massive instant messages (i.e. tweets) are posted every day. Twitter sentiment analysis tackles the problem of analyzing users’ tweets in terms of thoughts, interests and opinions in a variety of contexts and domains. Such analysis can be valuable for several researchers and applications that require understanding people views about a particular topic or event. The study carried out in this paper provides an overview of the algorithms and approaches that have been used for sentiment analysis in twitter. The reviewed articles are categories into four categories based on the approach they use. Furthermore, we discuss directions for future research on how twitter sentiment analysis approaches can utilize theories and technologies from other fields such cognitive science, semantic Web, big data and visualization.

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Section: Lexicon-based Approachesmentioning

confidence: 99%

Twitter Sentiment Analysis Approaches: A Survey

Adwan¹,

Al-Tawil²,

Huneiti³

et al. 2020

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

“…For example; customer satisfaction (Alharbi et al , 2018), consumer perception on mobile health applications (Pai and Alathur, 2018), evaluation of product review, e.g. instant noodle (Fernandes et al , 2019), skincare (Pugsee et al , 2019), movie (Azizan et al , 2019), airline (Dutta Das et al , 2017), exploring the recent issues, e.g. climate change (Dudani et al , 2020; Loureiro and Alló, 2020), politics (Tsaniya et al , 2021), Covid-19 (AlAgha, 2021; Flores-Ruiz et al , 2021; Sharma and Sharma, 2020) and others.…”

Section: Methodsmentioning

confidence: 99%

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Hassan

Hudaefi

Caraka

2021

SEF

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Purpose This paper aims to explore netizen’s opinions on cryptocurrency under the lens of emotion theory and lexicon sentiments analysis via machine learning. Design/methodology/approach An automated Web-scrapping via RStudio is performed to collect the data of 15,000 tweets on cryptocurrency. Sentiment lexicon analysis is done via machine learning to evaluate the emotion score of the sample. The types of emotion tested are anger, anticipation, disgust, fear, joy, sadness, surprise, trust and the two primary sentiments, i.e. negative and positive. Findings The supervised machine learning discovers a total score of 53,077 sentiments from the sampled 15,000 tweets. This score is from the artificial intelligence evaluation of eight emotions, i.e. anger (2%), anticipation (18%), disgust (1%), fear (3%), joy (15%), sadness (3%), surprise (7%), trust (15%) and the two sentiments, i.e. negative (4%) and positive (33%). The result indicates that the sample primarily contains positive sentiments. This finding is theoretically significant to measure the emotion theory on the sampled tweets that can best explain the social implications of the cryptocurrency phenomenon. Research limitations/implications This work is limited to evaluate the sampled tweets’ sentiment scores to explain the social implication of cryptocurrency. Practical implications The finding is necessary to explain the recent phenomenon of cryptocurrency. The positive sentiment may describe the increase in investment in the decentralised finance market. Meanwhile, the anticipation emotion may illustrate the public’s reaction to the bubble prices of cryptocurrencies. Social implications Previous studies find that the social signals, e.g. word-of-mouth, netizens’ opinions, among others, affect the cryptocurrencies’ movement prices. This paper helps explain the social implications of such dynamic of pricing via sentiment analysis. Originality/value This study contributes to theoretically explain the implications of the cryptocurrency phenomenon under the emotion theory. Specifically, this study shows how supervised machine learning can measure the emotion theory from data tweets to explain the implications of cryptocurrencies.

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“…Lexicon based approaches use dictionaries ( Hu & Liu, 2004 ) of words that are assumed to be positive, negative, or neutral, and then weigh or score the text according to the relative content of these words to infer its sentiment ( Akilandeswari & Jothi, 2018 ; Das et al, 2018 ; Azizan et al, 2019 ).…”

Section: Sentiment Analysis: Theoretical Backgroundmentioning

confidence: 99%

Sentiment analysis of vegan related tweets using mutual information for feature selection

Shamoi

Turdybay

Shamoi

et al. 2022

PeerJ Computer Science

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Nowadays, people get increasingly attached to social media to connect with other people, to study, and to work. The presented article uses Twitter posts to better understand public opinion regarding the vegan (plant-based) diet that has traditionally been portrayed negatively on social media. However, in recent years, studies on health benefits, COVID-19, and global warming have increased the awareness of plant-based diets. The study employs a dataset derived from a collection of vegan-related tweets and uses a sentiment analysis technique for identifying the emotions represented in them. The purpose of sentiment analysis is to determine whether a piece of text (tweet in our case) conveys a negative or positive viewpoint. We use the mutual information approach to perform feature selection in this study. We chose this method because it is suitable for mining the complicated features from vegan tweets and extracting users’ feelings and emotions. The results revealed that the vegan diet is becoming more popular and is currently framed more positively than in previous years. However, the emotions of fear were mostly strong throughout the period, which is in sharp contrast to other types of emotions. Our findings place new information in the public domain, which has significant implications. The article provides evidence that the vegan trend is growing and new insights into the key emotions associated with this growth from 2010 to 2022. By gaining a deeper understanding of the public perception of veganism, medical experts can create appropriate health programs and encourage more people to stick to a healthy vegan diet. These results can be used to devise appropriate government action plans to promote healthy veganism and reduce the associated emotion of fear.

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Lexicon-Based Sentiment Analysis for Movie Review Tweets

Cited by 11 publications

References 11 publications

Twitter Sentiment Analysis Approaches: A Survey

Twitter Sentiment Analysis Approaches: A Survey

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Sentiment analysis of vegan related tweets using mutual information for feature selection

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