An Accuracy Based Comparative Study on Different Techniques and Challenges for Sentiment Analysis

Jana, Radha Krishna; Maity, Saikat

doi:10.1007/978-981-19-2840-6_46

Cited by 2 publications

(2 citation statements)

References 59 publications

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“…There are various types of methods for sentiment analysis. Hybrid method gives the better accuracy than other methods (2) . Now a days people are busy in social networking site.…”

Section: Introductionmentioning

confidence: 95%

A Hybrid Approach to Analyse the Public Sentiment on Covid-19 Tweets

Jana,

Singh,

Maity

et al. 2024

IJST

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Objectives: The objective of this study is to introduce a hybrid model for analyzing the people sentiment on covid-19 tweets. Methods: We used a total no. of 27,500 datasets, 70% of the data sets for training and reserved the other 30% for testing. Due to this separation 19,250 samples are used for training, the remaining 8,250 were used to evaluate the accuracy of the test. This paper proposes a technique for sentiment analysis that integrates deep learning, genetic algorithms (GA), and social media sentiment. For more accuracy and performance, we here suggested a hybrid genetic algorithm-based model. A hybrid model is created by assembling the LSTM model and providing it to the genetic algorithm architecture. Findings: LSTM with a genetic model better than LSTM without genetic model. The accuracy of our suggested model is 96.40%. Novelty : The accuracy of the LSTM model for sentiment analysis is 91%. The accuracy of the proposed model is 96.40%. The proposed model is more accurate for sentiment prediction. Keywords: Social network perception, Crossover, Mutation, LSTM, NLP, GA

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“…There are various types of methods for sentiment analysis. Hybrid method gives the better accuracy than other methods (2) . Now a days people are busy in social networking site.…”

Section: Introductionmentioning

confidence: 95%

A Hybrid Approach to Analyse the Public Sentiment on Covid-19 Tweets

Jana,

Singh,

Maity

et al. 2024

IJST

View full text Add to dashboard Cite

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“…Sentiment analysis is a cutting-edge research discipline at the intersection of multiple fields, which includes social sciences, psychological sciences, deep learning, and data mining, and is an important component of natural language processing [2]. In recent years, researchers have proposed a large number of methods and models for different tasks such as sentence-level sentiment classification, aspect-level sentiment classification, opinion target extraction, and multimodal sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis of Chinese game reviews based on SKEP

Wang¹

2023

AETR

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As China's game industry enters the era of the stock market, improving the quality of game products in all aspects is gradually becoming an important part of the development of China's game industry. There exists a large amount of game review data on the Internet, and sentiment analysis of them can provide important help for game makers to make decisions. However, the huge scale of game reviews and the extensive use of metaphors and sarcasm make it difficult to conduct sentiment analysis. In this paper, we experiment with a publicly available Chinese game review dataset using the ERNIE pre-training model equipped with the SKEP method, which improves the model's masking strategies and pre-training objectives to specialize the model's sentiment analysis ability. In addition, we compared the performance of ERNIE1.0, RoBERTa, MacBERT, BERT-wwm, and Nezha. The precision, recall, and F1 score of SKEP are higher than those of the models for comparison studies, which confirms the effectiveness of SKEP in this area.

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An Accuracy Based Comparative Study on Different Techniques and Challenges for Sentiment Analysis

Cited by 2 publications

References 59 publications

A Hybrid Approach to Analyse the Public Sentiment on Covid-19 Tweets

A Hybrid Approach to Analyse the Public Sentiment on Covid-19 Tweets

Sentiment analysis of Chinese game reviews based on SKEP

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