Review on Sentiment Lexicons

Jagdale, Rajkumar S.; Shirsat, Vishal S.; Deshmukh, Sachin N.

doi:10.1109/cesys.2018.8723913

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Referring to related practices and research at home and abroad, this paper fully evaluates the performance of the sentiment lexicon from two aspects: dictionary performance and application effect (Jagdale et al , 2018). The former is to evaluate the accuracy of the sentiment words in the sentiment lexicon.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The sentiment lexicon, which contains words and phrases with their sentiment polarity, plays an important role in sentiment analysis tasks (Alaei et al , 2019; Jagdale et al , 2018; Liu et al , 2020). Many types of research have proven that sentiment words have obvious domain dependence (Mishev et al , 2020; Park et al , 2015; Peng et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

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Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Zha

Shi

2022

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Purpose Sentiment lexicon is an essential resource for sentiment analysis of user reviews. By far, there is still a lack of domain sentiment lexicon with large scale and high accuracy for Chinese book reviews. This paper aims to construct a large-scale sentiment lexicon based on the ultrashort reviews of Chinese books. Design/methodology/approach First, large-scale ultrashort reviews of Chinese books, whose length is no more than six Chinese characters, are collected and preprocessed as candidate sentiment words. Second, non-sentiment words are filtered out through certain rules, such as part of speech rules, context rules, feature word rules and user behaviour rules. Third, the relative frequency is used to select and judge the polarity of sentiment words. Finally, the performance of the sentiment lexicon is evaluated through experiments. Findings This paper proposes a method of sentiment lexicon construction based on ultrashort reviews and successfully builds one for Chinese books with nearly 40,000 words based on the Douban book. Originality/value Compared with the idea of constructing a sentiment lexicon based on a small number of reviews, the proposed method can give full play to the advantages of data scale to build a corpus. Moreover, different from the computer segmentation method, this method helps to avoid the problems caused by immature segmentation technology and an imperfect N-gram language model.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Zha

Shi

2022

View full text Add to dashboard Cite

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“…The evaluation of the sentimental effect is conducted by matching the words to the existing lexicons (Jagdale et al 2018) and look up for their sentiment score in the lexicon.…”

Section: Sentiment Analysismentioning

confidence: 99%

Text mining and sentiment analysis of COVID-19 tweets

Zhang,

Yi,

Chen

et al. 2021

Preprint

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The human severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), causing the COVID-19 disease, has continued to spread all over the world. It menacingly affects not only public health and global economics but also mental health and mood. While the impact of the COVID-19 pandemic has been widely studied, relatively fewer discussions about the sentimental reaction of the population have been available. In this article, we scrape COVID-19 related tweets on the microblogging platform, Twitter, and examine the tweets from Feb 24, 2020 to Oct 14, 2020 in four Canadian cities (Toronto, Montreal, Vancouver, and Calgary) and four U.S. cities (New York, Los Angeles, Chicago, and Seattle). Applying the Vader and NRC approaches, we evaluate the sentiment intensity scores and visualize the information over different periods of the pandemic. Sentiment scores for the tweets concerning three anti-epidemic measures, masks, vaccine, and lockdown, are computed for comparisons. The results of four Canadian cities are compared with four cities in the United States. We study the causal relationships between the infected cases, the tweet activities, and the sentiment scores of COVID-19 related tweets, by integrating the echo state network method with convergent cross-mapping. Our analysis shows that public sentiments regarding COVID-19 vary in different time periods and locations. In general, people have a positive mood about COVID-19 and masks, but negative in the topics of vaccine and lockdown. The causal inference shows that the sentiment influences people's activities on Twitter, which is also correlated to the daily number of infections.

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Twitter Sentiment Analysis on Government Law Using Real Time Data

Patil

Wagh

Bhinge

et al. 2021

IJSRST

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Social media monitoring has been growing day by day so analyzing social data plays an important role in knowing people's behavior. So we are analyzing Social data such as Twitter Tweets using sentiment analysis which checks the opinion of people related to government schemes that are announced by the Central Government. This paper-based is on social media Twitter datasets of particular schemes and their polarity of sentiments. The popularity of the Internet has been rapidly increased. Sentiment analysis and opinion mining is the field of study that analyses people's opinions, sentiments, evaluations, attitudes, and emotions from written language. User-generated content is highly generated by users. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. It is difficult to analyze or summarize user-generated content. Most of the users write their opinions, thoughts on blogs, social media sites, E-commerce sites, etc. So these contents are very important for individuals, industry, government, and research work to make decisions. This Sentiment analysis and opinion mining research is a hot research area that comes under Natural Language processing. We plot and calculate numbers of positive, negative, and neutral tweets from each event.

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Review on Sentiment Lexicons

Cited by 3 publications

References 9 publications

Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Text mining and sentiment analysis of COVID-19 tweets

Twitter Sentiment Analysis on Government Law Using Real Time Data

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