Sentiment analysis of movie review data using Senti-lexicon algorithm

Mumtaz, Deebha; Ahuja, Bindiya

doi:10.1109/icatcct.2016.7912069

Cited by 23 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2018) explains that there are data problems contained in the current movie review which causes sentiment analysis to be very slow and less sensitive. In this study, the author performs feature selection to choose to use Information Gain to eliminate features that are (Mumtaz, D., & Ahuja, B. 2016) proposed a Senti-lexical algorithm to find the polarity of the review as positive, negative, or neutral.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Analysis Sentiment Based on IMDB Aspects from Movie Reviews using SVM

Ramadhan¹,

Ramadhan²

2022

SinkrOn

View full text Add to dashboard Cite

A movie is a spectacle that can be done at a relaxed time. Currently, there are many movies that can be watched via the internet or cinema. Movies that are watched on the internet are sometimes charged to watch so that potential viewers before watching a movie will read comments from users who have watched the movie. The website that is often used to view movie comments today is IMDB. Movie comments are many and varied on the IMDB website, we can see comments based on the star rating aspect. This causes users to have difficulty analyzing other users' comments. So, this study aims to analyze the sentiment of opinions from several comments from IMDB website users using the star rating aspect and will be classified using the support vector machine method (SVM). Sentiment analysis is a classification process to understand the opinions, interactions, and emotions of a document or text. SVM is very efficient for many applications in science and engineering, especially for classification (pattern recognition) problems. In addition to the SVM method, the TF-IDF technique is also used to change the shape of the document into several words. The results obtained by applying the SVM model are 79% accuracy, 75% precision, and 87% recall. The SVM classification is also superior to other methods, namely logistic regression.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…2016) proposed a Senti-lexical algorithm to find the polarity of the review as positive, negative, or neutral. The author has also proposed a method for dealing with words that hurt the review and the role of emoticons is also discussed (Mumtaz, D., & Ahuja, B. 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis Sentiment Based on IMDB Aspects from Movie Reviews using SVM

Ramadhan¹,

Ramadhan²

2022

SinkrOn

View full text Add to dashboard Cite

show abstract

“…The customers' sentiment is basically influenced by the customers' satisfaction. Maks & Vosen [16] found that sentiment analysis tools perform better compared to reviewer ratings. This finding is also supported by Bhatt et.…”

Section: Sentiment Analysismentioning

confidence: 99%

Comparative Study on Sentiment Analysis Approach for Online Shopping Review

et.al¹

2021

TURCOMAT

View full text Add to dashboard Cite

The internet has revolutionized the way most people shop. Flexibility, convenience, products’ variations, better price, and more privacy contribute to the exponential growth of online shopping platforms. However, due to the nature of online shopping, customers are not able to physically test the product before purchasing. They rely on the information given by the seller and previous customers’ ratings to make their decision. Sometimes, the information that is given by sellers may be fraudulent, misleading, or over claim. Many researchers had found out that ratings and other customers’ reviews can be manipulated and did not reflect on the actual customers’ sentiment on the particular product. This research investigates how sentiment analysis can be used as an alternative solution to measure the positive, negative, and neutral feedback of the past reviews. It is to offer more comprehensive way to help the customers make an informed decision for the product that they wish to buy based on the totality of the reviews. This paper makes a comparative study on sentiment analysis methods on online shopping reviews. This can lead to the proposed theoretical framework of an alternative solution for better insight exploration. It is envisaged that this research would benefit the customer in making a better decision when doing online shopping and may act as a feedback mechanism for the seller to provide good products and services. A good product rating can influence many new buyers and increase business revenue and expansion

show abstract

“…The sentiment is an attitude or perception driven by feeling. Sentiment analysis (SA) is the process of determining and measuring the emotional state of response made by opinion [8][9][10][11]. SA is a tool that enables companies to analyze what their customers are thinking about their products and services and identifies the trends in the behavior or attitude of their customers toward the products and services they provide concerning their competitors.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…The lexical-based and machine learning-based approaches are combined in [13] with the aim of introducing a hybrid architecture with higher accuracy than the pure lexical method and provides more structure and increased redundancy than machine learning approach. The Senti-lexical algorithm [9] to find the polarity of a review as positive, negative or neutral is proposed to handle words which have negation effect on the reviews. The role of emoticons is also discussed in this paper.…”

Section: Literature Reviewmentioning

confidence: 99%

Opinion Mining of GST Implementation using Supervised Machine Learning Approach

Tomar¹,

Srivastava²,

Ahuja³

2018

IJCA

Self Cite

View full text Add to dashboard Cite

Sentiment Analysis is a way to determine the emotions behind social media discussions. Analyzing social data plays a vital role in knowing people"s behavior about an entity or event occurring in the society. Sentiment analysis is widely used in a variety of applications like classifying, summarizing and aggregating reviews from the massive amount of unstructured data that may be available from customer comments, blogs, feedback and reviews on any product or social issue. The Goods & Service Tax(GST) was debated a lot in the social network as it impacts every citizen of India and there was a state of confusion among people about this amendment in the taxation system. If this state of people can be determined, then it can help in identifying how effectively GST scheme is executing. In this paper, we present Sentiment Analysis (SA) of GST by using the textual content of Twitter to determine the public opinion about GST.

show abstract

Sentiment analysis of movie review data using Senti-lexicon algorithm

Cited by 23 publications

References 14 publications

Analysis Sentiment Based on IMDB Aspects from Movie Reviews using SVM

Analysis Sentiment Based on IMDB Aspects from Movie Reviews using SVM

Comparative Study on Sentiment Analysis Approach for Online Shopping Review

Opinion Mining of GST Implementation using Supervised Machine Learning Approach

Contact Info

Product

Resources

About