Language Independent Multi-Class Sentiment Analysis

Kundale, Jyoti; Kulkarni, Nikita

doi:10.1109/iccubea47591.2019.9128383

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…After the classification, the accuracy will be found out based on the model or the algorithm. Later, the Ternary Classification [1][2][3]8] came into existence where the sentiments will be categorized into 3 Classes like Positive, Negative and Neutral. All the data which are neither Positive nor Negative will be classified using the Neutral Class.…”

Section: Multi-class Sentiment Analysismentioning

confidence: 99%

“…We can conclude that a Multi-class Classification performs well as it gives a precise classification as the data is organized into different subclasses or polarity based on the dataset. Senti-WordNet [3,8] dictionary is used to identify the positivity and negativity or the sentiments of the sentence. Ali Shariq Imran et al [40], Sentiment analysis on tweets refers to the classification of an input tweet text into sentiment polarities, including positive, negative and neutral, whereas emotions' classification refers to classifying tweet text in emotions' label including joy, surprise, sadness, fear, anger and disgust.…”

Section: Multi-class Sentiment Analysismentioning

confidence: 99%

“…Stemming: Stemming [3,8] is the process of identifying the words that are similar in meaning.It will help in reducing the redundancy to get the base form of the word by removing the suffix.…”

Section: Negation Handlingmentioning

confidence: 99%

“…Table 3 shows examples of emoticons. Like textual data, emoticons and emojis [3,9,11,14] also can be used for the sentiment analysis. They also have some sentiment score values, which can be considered or used for sentiment analysis.…”

Section: Emoticons and Emoji Analysismentioning

confidence: 99%

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Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

2021

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Section: Multi-class Sentiment Analysismentioning

confidence: 99%

Section: Multi-class Sentiment Analysismentioning

confidence: 99%

Section: Negation Handlingmentioning

confidence: 99%

Section: Emoticons and Emoji Analysismentioning

confidence: 99%

See 2 more Smart Citations

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

2021

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“…Two methods of measurement commonly used for classification performance analysis are "recall" and "precision". Both measures are useful for feature extraction, opinion phrase extraction, and information retrieval [27]. Precision is the proportion of the relevant prediction result.…”

Section: Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter

Habibi

Priadana

Ma’arif

2021

IJID

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The World Health Organization (WHO) declared the COVID-19 outbreak has resulted in more than six million confirmed cases and more than 371,000 deaths globally on June 1, 2020. The incident sparked a flood of scientific research to help society deal with the virus, both inside and outside the medical domain. Research related to public health analysis and public conversations about the spread of COVID-19 on social media is one of the highlights of researchers in the world. People can analyze information from social media as supporting data about public health. Analyzing public conversations will help the relevant authorities understand public opinion and information gaps between them and the public, helping them develop appropriate emergency response strategies to address existing problems in the community during the pandemic and provide information on the population's emotions in different contexts. However, research related to the analysis of public health and public conversations was so far conducted only through supervised analysis of textual data. In this study, we aim to analyze specifically the sentiment and topic modeling of Indonesian public conversations about the COVID-19 on Twitter using the NLP technique. We applied some methods to analyze the sentiment to obtain the best classification method. In this study, the topic modeling was carried out unsupervised using Latent Dirichlet Allocation (LDA). The results of this study reveal that the most frequently discussed topic related to the COVID-19 pandemic is economic issues.

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Analysis of E-learner’s Opinion Using Automated Sentiment Analysis in E-learning and Comparison with Naive Bayes Classification, Random Forest and K-Nearest Neighbour Algorithms

Rajesh

Suseendran

2021

Intelligent Computing and Innovation on Data Science

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Language Independent Multi-Class Sentiment Analysis

Cited by 8 publications

References 9 publications

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter

Analysis of E-learner’s Opinion Using Automated Sentiment Analysis in E-learning and Comparison with Naive Bayes Classification, Random Forest and K-Nearest Neighbour Algorithms

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