A Survey on Opinion Mining and Sentiment Analysis

Nimesh, R.; Raghava, P. Veera; Mary, S. Prince; Bharathi, B.

doi:10.1088/1757-899x/590/1/012003

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…Sentiment analysis is one of the natural language processing methods whose purpose is to find out the emotional sentiment of the analyzed text (Nimesh et al, 2019). Research on sentiment analysis has grown since 2003 and is part of Text Mining which is computational research based on sentiment, emoticons, opinions, comments and any expressions expressed by text.…”

Section: Sentiment Analysismentioning

confidence: 99%

Twitter Sentiment Analysis on Halal Cosmetics

Siti Maysyaroh,

Aam Slamet Rusydiana

2023

IMR

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This study aims to analyze sentiments related to halal cosmetic products through data taken from the Twitter social media platform. The method used in this research is sentiment analysis using a Python library known as VADER (Valence Aware Dictionary and Sentiment Reasoner). The data retrieved includes tweets with keywords related to "halal cosmetics" in the period January 1, 2019 to March 28, 2023. The results of this sentiment analysis reveal that the majority of sentiments related to halal cosmetics are positive, with a percentage of around 56.8%. Furthermore, about 36.1% of the detected sentiments are neutral, indicating that most consumers have a neutral view towards halal cosmetic products. Negative sentiments have a percentage of 7.1%, indicating that there is a small percentage of users or consumers who may have problems or negative experiences related to these products. The results of this study provide a better understanding of how consumers respond to halal cosmetic products on the social media platform Twitter, as well as identify opportunities and challenges within this market. The implications of these findings can help manufacturers and stakeholders in the halal cosmetics industry to improve consumer understanding, address emerging issues, and support the growth of this rapidly evolving industry.

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Section: Sentiment Analysismentioning

confidence: 99%

Twitter Sentiment Analysis on Halal Cosmetics

Siti Maysyaroh,

Aam Slamet Rusydiana

2023

IMR

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“…Sentiment analysis is one of the natural language processing methods whose purpose is to find out the emotional sentiment of the analyzed text. (Nimesh et al, 2019). Research on sentiment analysis has grown since 2003 and is part of Text Mining which is computational research based on sentiment, emoticons, opinions, comments and any expressions expressed by text.…”

Section: Sentiment Analysismentioning

confidence: 99%

Digital Zakat

Annisa Kartika Ocktavia,

Assyifa Nur Aziza,

Ihsanul Ikhwan

2023

IMR

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This research was conducted to examine the perception of digital zakat in the scope of Islamic economics from the point of view of sentiment research on Twitter social media users in responding to the development of digital zakat. The method used in this research is a qualitative approach with descriptive statistics from literature studies in the form of 1237 Twitter tweets. Python library software called VADER (Valence Aware Dictionary and Sentiment Reasoner) is used to classify tweets. The sentiment analysis results tended to perceive positive sentiment at 51.7%, neutral sentiment at 28.6% and negative sentiment at 19.7%. The variety of sentiment analysis results reflects the diversity of opinions regarding the progress of digital zakat. The benefit of this study is to contribute literature and considerations in relation to the development of digital zakat as well as identifying shortcomings and threats, such as negative perceptions towards the use of digital zakat. In addition, the objectives of this study also include understanding the positive views, advantages, potentials, and benefits of digital zakat. This research is the first comprehensive research that discusses sentiment analysis on the theme of digital zakat with Twitter tweet data. Suggestions for further research and recommendations are included.

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“…The door [71] the author used the Conditional Randomness Fields (CRF) method to extract comments through manual learning techniques. This is also by author [72] to determine the compatibility of a multidisciplinary polar pattern Algorithm used in Chinese language studies. They work on different elements and get good results.…”

Section: Corpus-based Approachmentioning

confidence: 99%

A Survey: Sentimental Analysis on Product Reviews Using (MLT) Machine Learning Techniques and Approaches

2021

IJATCSE

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Social media sites are used today for the development of different types and nature of customers those use such benefits which are often shared by people on social media symbolic or textual opinions, ideas, and feelings. This attitude and orientation draw attention to research and analyze sentiments through online data about customer interest. Therefore, the sentimental analysis idea is proposed. This is among the various uses of Natural Language Processing (NLP) and Machine Learning Analysis (MLA) is very common. The main task of sentimental analysis is the classification of sentiments automatically into three categories that are positive, negative and neutral. Many classification researches are conducted over the years to know the exact feelings and situations of sentimental emotions of people. Classification, fuzzy and clustering, is used. To know the sentiment analysis of the people’s accurate feeling and situation, many times over the years classification research was conducted in past. The accuracy of classification is finding more in Fuzzy based. Fuzzy based classification finds more accurate and for comparative study execution Classical Text Classifications Model is used. In comparative performance, this study shows the possibility of implementing the proposed method able to provide more accurate results when it comes in comparison with conventional classifiers. In this article we have discussed different researchers worked on the method of sentiment analysis and classification. This article also shows the importance of extracting comments and analyze sentiments

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A Survey on Opinion Mining and Sentiment Analysis

Cited by 10 publications

References 5 publications

Twitter Sentiment Analysis on Halal Cosmetics

Twitter Sentiment Analysis on Halal Cosmetics

Digital Zakat

A Survey: Sentimental Analysis on Product Reviews Using (MLT) Machine Learning Techniques and Approaches

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