Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method

Kumar, H. M. Keerthi; Harish, B. S.; Hs, Darshan

doi:10.9781/ijimai.2018.12.005

Cited by 74 publications

(37 citation statements)

References 21 publications

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“…We used text messages in discussion boards as a proxy for determining students' emotions (Dehkharghani, 2018;Kumar, Harish, & Darshan, 2019;Garcia-Garcia, Penichet, & Lozano, 2017). In the context of online discussions, learning leaders use their text messages to express their emotions as well as recognize and respond to others' emotions (Author, 2019b).…”

Section: Discussionmentioning

confidence: 99%

How students emerge as learning leaders in small group online discussions

Kim

Lee

Wang

2020

Computer Assisted Learning

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In this study, we examined the role of leadership styles and multi‐dimensional learner engagement in how students emerge as learning leaders in asynchronous online discussions. Grounded in the conceptual framework of two dominant leadership styles of transformational and transactional leadership, this study applies the two leadership styles—transformational leadership and transactional leadership—to the Leader Identification Method (LIM) which defines three types of leader roles (i.e., full, transactional and attractive facilitator) in online learning. We collected data from 20 students enrolled in a graduate‐level online course that required participation in 12‐week discussion activities. Results of the longitudinal data analyses show that person‐focused, transformational leadership and active participation in online discussions were significant factors that enabled students to emerge as learning leaders. Students are more likely to become leaders by exhibiting transformational leadership behaviour and productively interacting with one another in an online discussion community.

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Section: Discussionmentioning

confidence: 99%

How students emerge as learning leaders in small group online discussions

Kim

Lee

Wang

2020

Computer Assisted Learning

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“…Sentiment analysis is the technique of identifying significant and individual data from a collection of source materials that contain unacknowledged information of people's perspectives [1]. People usually go over blogs, social networking Web sites like Facebook and Twitter and review sites like IMDB and rotten tomatoes to know about the cast and crew of the movie, review, and ratings [2]. To automate the organization and categorization of sentiment from sentiments reviews, sentiment analysis uses text analysis, natural language processing (NLP), and computational techniques [3].…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis on IMDB using lexicon and neural networks

et al. 2020

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To find out what other people think has been an essential part of information-gathering behaviors. And in the case of movies, the movie reviews can provide an intricate insight into the movie and can help decide whether it is worth spending time on. However, with the growing amount of data in reviews, it is quite prudent to automate the process, saving on time. Sentiment analysis is an important field of study in machine learning that focuses on extracting information of subject from the textual reviews. The area of analysis of sentiments is related closely to natural language processing and text mining. It can successfully be used to determine the attitude of the reviewer in regard to various topics or the overall polarity of the review. In the case of movie reviews, along with giving a rating in numeric to a movie, they can enlighten us on the favorableness or the opposite of a movie quantitatively; a collection of those then gives us a comprehensive qualitative insight on different facets of the movie. Opinion mining from movie reviews can be challenging due to the fact that human language is rather complex, leading to situations where a positive word has a negative connotation and vice versa. In this study, the task of opinion mining from movie reviews has been achieved with the use of neural networks trained on the "Movie Review Database" issued by Stanford, in conjunction with two big lists of positive and negative words. The trained network managed to achieve a final accuracy of 91%.

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“…Mining and utilizing these reviews are of great significance By analyzing the sentiment of user reviews, website operators or merchants can analyze the pros and cons of a product, speculate on users' preferences to develop a reasonable marketing strategy, and propose plans to improve the product's reputation and profitability. Producers of literary works such as movies (Kumar et al 2019) and the tourism industry (Afzaal et al 2019) can also understand popular trends and users' perceptions for the works. These reviews, which reflect users' opinions and sentimental tendencies, are increasingly of great value.…”

Section: Introductionmentioning

confidence: 99%

Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

Wei¹,

Liu²,

Wei³

2020

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Mining product reviews and sentiment analysis are of great significance, whether for academic research purposes or optimizing business strategies. We propose a feature-level sentiment analysis framework based on rules parsing and fine-grained domain ontology for Chinese reviews. Fine-grained ontology is used to describe synonymous expressions of product features, which are reflected in word changes in online reviews. First, a semiautomatic construction method is developed by using Word2Vec for fine-grained ontology. Then, feature-level sentiment analysis that combines rules parsing and the fine-grained domain ontology is conducted to extract explicit and implicit features from product reviews. Finally, the domain sentiment dictionary and context sentiment dictionary are established to identify sentiment polarities for the extracted feature-sentiment combinations. An experiment is conducted on the basis of product reviews crawled from Chinese e-commerce websites. The results demonstrate the effectiveness of our approach.

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Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method

Cited by 74 publications

References 21 publications

How students emerge as learning leaders in small group online discussions

How students emerge as learning leaders in small group online discussions

Sentiment analysis on IMDB using lexicon and neural networks

Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

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