Sentiment Analysis in Social Media

Yusof, Noor Fazilla Abd; Lin, Chenghua; He, Yulan

doi:10.1007/978-1-4939-7131-2_120

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…However, XGBoost gives the same performance for both TF-IDF and BOW. The error rate can be calculated using Mean Square error, but it is a language-based analysis, and the verification of results can be done manually only so we can identify the best model [56]. We don't have any similar data research which can show the results, so we need to base on the native reader for it.…”

Section: Resultsmentioning

confidence: 99%

A Rule-Based Sentiment Analysis of WhatsApp Reviews in Telugu Language

Passi

Kalakala

2022

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Sentiment analysis is one of the major fields of research for any case regarding natural language processing. For this purpose, the data is often some form of review or a feedback so that the emotion and the main sentiment behind the feedback can be assessed using machine learning techniques. A similar approach is performed in this research, In this report, Whatsapp reviews of customers in Telugu language were analysed and the sentiment polarity was calculated using a rule based approach. Telugu language is from the southern part of India and uses different sets of fonts from the general sets. The strings are treated as similarly as they are in any other machine learning process since the meaning behind them is captured through the patterns that emerge from the text. All the text processing is carried oput similarly to most NLP scenarios. To find out the overall sentiment in the review that is collected from the internet, a manual rule-based algorithm is developed which can apply certain sets of rules to a sentence to check the polarity, which can be positive, negative, or neutral. These rules check the presence of words such as major negative words and major positive words, and even auxiliary verbs and their position with respect to the negative and positive words. This rule-based approach was then used to train a machine learning model using a few parametric classifiers like K-nearest neighbours (KNN), XGBoost and support vector machines (SVM). The classifiers also fetched a decent accuracy of 81%, 82% and 78% respectively, which indicated towards the good performance of the rule-based approach and its effectiveness with error counts of 0.296, 0.288 and 0.252 with TF-IDF and 0.285, 0.285 iv and 0.234 with Bag of Words. Along the process, manual observation was also used to compare the assigned sentiments to the sentence to find the errors in the method. The best performance with respect to results was given by SVM classifiers that returned an f1 score of 79% and the lowest error count of 0.23 which is better among all the classifiers. The metrics which were used to judge these classifiers were the precision, recall, f1 scores and the mean squared error.

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Section: Resultsmentioning

confidence: 99%

A Rule-Based Sentiment Analysis of WhatsApp Reviews in Telugu Language

Passi

Kalakala

2022

IOT With Smart Systems

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“…Social media data can help with disaster management [72], detecting traffic accidents [73]. Sentiment analysis in social media analysis allows monitoring social media users opinions about selected products or services or identifying reputations in the context of their competitors and providing them with insight into emerging trends and potential changes in market opinions [74].…”

Section: Social Media Analysismentioning

confidence: 99%

Healthy Food on Instagram Social Network: Vegan, Homemade and Clean Eating

et al. 2021

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Social media platforms have become part of many people’s lives. Users are spending more and more time on these platforms, creating an active and passive digital footprint through their interaction. This footprint has high research potential in many research areas because understanding people’s communication on social media is essential in understanding their values, attitudes, experiences and behaviors. Researchers found that the use of social networking sites impacts adolescents’ eating behavior. If we define adolescents as individuals between ages 10 and 24 (WHO’s definition), 76% of USA young people at age 18–⁠24 use Instagram, so the Instagram social network analysis is important for understanding young people’s expressions in the context of healthy food. This study aims to identify the main topic associated with healthy food on the Instagram social network via hashtag and community analysis based on 2,045,653 messages created by 427,936 individual users. The results show that users most associate Healthy food with healthy lifestyle, fitness, weight loss and diet. In terms of food, these are foods that are Vegan, Homemade, Clean and Plant-based. Given that young people change their behavior in relation to people’s behavior on social networks, it is possible to use this data to predict their future association with healthy food characteristics.

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“…Support Vector Machines (SVM) are discriminative classifiers that make use of statistical learning theory [71,72] to find distinct optimal separating hyperplane between two classes [73]. This is achieved by locating the maximum margin between the classes' closest points.…”

Section: Support Vector Machinesmentioning

confidence: 99%