Sentiment analysis on movie review through building modified sentiment dictionary by movie genre

Lee, Sang Hoon; Cui, Jing; Kim, Jong Woo

doi:10.13088/jiis.2016.22.2.097

Cited by 26 publications

(12 citation statements)

References 5 publications

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“…Additionally, the context is known to affect the emotional interpretation of terms, as previously studied [34], [37]. There exist initiatives that try to schematize or represent semantic and lexematic analysis of suspense-related genres, but the results are inconclusive and are not available as implementations [68], [69]. This does not mean that relying on semantic analysis tools implies a lack of precision (in fact, it has been observed that it increases precision).…”

Section: Discussionmentioning

confidence: 99%

Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

2021

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The perception of suspense in stories is affected not only by general literary aspects like narrative structure and linguistic features, but also by anticipation and evocation of feelings like aversion, disgust or empathy. As such, it is possible to alter the feeling of suspense by modifying components of a story that convey these feelings to the audience. Based on a previous straightforward model of suspense adaptation, this paper describes the design, implementation and evaluation of a computational system that adapts narrative scenes for conveying a specific user-defined amount of suspense. The system is designed to address the impact of different types of emotional components on the reader. The relative weighted suspense of these components is computed with a regression model based on a sentiment analysis tool, and used as a fitness function in an evolutionary algorithm. This new function is able to identify the different weights on the prediction of suspense in aspects like outcome, decorative elements, or threat's appearance. The results indicate that this approach represents a significant improvement over the previous existing approach.

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Section: Discussionmentioning

confidence: 99%

Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

2021

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“…Sentiment analysis is a text mining technique that extracts the key opinions, emotions, attitudes, and dispositions from a large amount of text data to estimate and classify the author’s emotions (Feldman, 2013 ; Lee et al, 2016 ). In general, the data are classified in a binary form as positive or negative and are further sub-categorized into multi-category sensibilities such as sadness, anger, or happiness.…”

Section: Methodsmentioning

confidence: 99%

Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis

et al. 2021

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This study used several informatics techniques to analyze consumer-driven social media data from four cities (Paris, Milan, New York, and London) during the 2019 Fall/Winter (F/W) Fashion Week. Analyzing keywords using a semantic network analysis method revealed the main characteristics of the collections, celebrities, influencers, fashion items, fashion brands, and designers connected with the four fashion weeks. Using topic modeling and a sentiment analysis, this study confirmed that brands that embodied similar themes in terms of topics and had positive sentimental reactions were also most frequently mentioned by the consumers. A semantic network analysis of the tweets showed that social media, influencers, fashion brands, designers, and words related to sustainability and ethics were mentioned in all four cities. In our topic modeling, the classification of the keywords into three topics based on the brand collection’s themes provided the most accurate model. To identify the sentimental evaluation of brands participating in the 2019 F/W Fashion Week, we analyzed the consumers’ sentiments through positive, neutral, and negative reactions. This quantitative analysis of consumer-generated social media data through this study provides insight into useful information enabling fashion brands to improve their marketing strategies.

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“…), (b) the internal data warehouse that captures the routing-related information for past customers, (c) external linguistic sources such as industryspecific ontologies and dictionaries. Using such external sources to create context-sensitive semantic and sentiment features is common in industries such as finance (Das & Chen, 2007;Loughran & McDonald, 2011), entertainment (Lee, Cui, & Kim, 2016), and manufacturing (Abrahams et al, 2015;Abrahams, Jian, Alan Wang, & Fan, 2012). The IVR data connection is kept open during system operations due to real-time customer routing decisions; whereas access to the second and third data sources are only required when learning or updating the classification function γ.…”

Section: Implementation Detailsmentioning

confidence: 99%

Improving customer routing in contact centers: An automated triage design based on text analytics

Ilk

Shang

Góes

2020

J of Ops Management

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We propose an automated triage design for intelligent customer routing in live-chat contact centers and demonstrate its implementation using a realworld data set from an S&P 500 firm. The proposed design emerges as a synthesis of text analytics and predictive machine learning methods. Using numerical experiments based on the simulation of the firm's contact center, we demonstrate the service level, time, and labor cost benefits of the automated design over two other triage designs (i.e., customer choice triage and human expert triage) that are commonly employed in the real world. Through additional analyses, we explore the generalizability of the automated design for creating solutions for different types of communication channels. Our work has implications for managing customer relations under emerging communication technologies (e.g., live-chat, e-mail, and social media) and more broadly for demonstrating the use of text analytics and machine learning to improve Operations Management practice.

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Sentiment analysis on movie review through building modified sentiment dictionary by movie genre

Cited by 26 publications

References 5 publications

Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis

Improving customer routing in contact centers: An automated triage design based on text analytics

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