Classification of sentiments in short-text

Kumar, H. M. Keerthi; Harish, B. S.; Kumar, S. V. Aruna; Aradhya, V. N. Manjunath

doi:10.1145/3184066.3184074

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Short texts like Reddit posts and tweets are challenging analyzing and determine sentiment as they have a restriction on the number of characters which leads to the use of acronyms, slangs, and misspellings by users [33]. The SentiStrength [34] and TensiStrength [35] algorithms were developed by Thelwall and colleagues to measure the intensity of the positive and negative words and the intensity of stress and relaxation using a lexical approach.…”

Section: Related Workmentioning

confidence: 99%

Mental toll on working women during the COVID-19 pandemic: An exploratory study using Reddit data

et al. 2023

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COVID-19 has led to an unprecedented surge in unemployment associated with increased anxiety, stress, and loneliness impacting the well-being of various groups of people (based on gender and age). Given the increased unemployment rate, this study intends to understand if the different dimensions of well-being change across age and gender. By quantifying sentiment, stress, and loneliness with natural language processing tools and one-way, between-group multivariate analysis of variance (MANOVA) using Reddit data, we assessed the differences in well-being characteristics for age groups and gender. We see a noticeable increase in the number of mental health-related subreddits for younger women since March 2020 and the trigger words used by them indicate poor mental health caused by relationship and career challenges posed by the pandemic. The MANOVA results show that women under 30 have significantly (p = 0.05) higher negative sentiment, stress, and loneliness levels than other age and gender groups. The results suggest that younger women express their vulnerability on social media more strongly than older women or men. The huge disruption of job routines caused by COVID-19 alongside inadequate relief and benefit programs has wrecked the economy and forced millions of women and families to the edge of bankruptcy. Women had to choose between being home managers and financial providers due to the countrywide shutdown of schools and day-cares. These findings open opportunities to reconsider how policy supports women’s responsibilities.

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Section: Related Workmentioning

confidence: 99%

Mental toll on working women during the COVID-19 pandemic: An exploratory study using Reddit data

et al. 2023

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“…Classification is the process of assigning labels to the reviews whose label is unknown. In the proposed work, supervised learning algorithms such as Naïve Bayes [17], Maximum Entropy [16], Support Vector Machines (SVM) [13] and K-Nearest Neighbor (KNN) [26] [30] are used. Naïve Bayes and Maximum Entropy work on the principles of probability and hence they are known as probabilistic classifiers.…”

Section: Classificationmentioning

confidence: 99%

Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method

Kumar¹,

Harish²,

Hs³

2019

IJIMAI

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Social Networking sites have become popular and common places for sharing wide range of emotions through short texts. These emotions include happiness, sadness, anxiety, fear, etc. Analyzing short texts helps in identifying the sentiment expressed by the crowd. Sentiment Analysis on IMDb movie reviews identifies the overall sentiment or opinion expressed by a reviewer towards a movie. Many researchers are working on pruning the sentiment analysis model that clearly identifies and distinguishes between a positive review and a negative review. In the proposed work, we show that the use of Hybrid features obtained by concatenating Machine Learning features (TF, TF-IDF) with Lexicon features (Positive-Negative word count, Connotation) gives better results both in terms of accuracy and complexity when tested against classifiers like SVM, Naïve Bayes, KNN and Maximum Entropy. The proposed model clearly differentiates between a positive review and negative review. Since understanding the context of the reviews plays an important role in classification, using hybrid features helps in capturing the context of the movie reviews and hence increases the accuracy of classification.

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