Automatic Construction of a Negative/positive Corpus and Emotional Classification using the Internet Emotional Sign

Jang, Kyoung‐Ae; Park, Sang‐Hyun; Kim, Woo-Je

doi:10.5626/jok.2015.42.4.512

Cited by 11 publications

(5 citation statements)

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“…In general, it is used to classify emotions expressed in texts or to convert them into objective numerical data. In a narrow sense, it can be seen as classifying positive and negative emotions in the text [44]. In addition, this analysis method includes not only simply classifying positive and negative, but also analyzing the intention or stance of the writer by extracting positive and negative words [45].…”

Section: Big Data In the Food Service Industrymentioning

confidence: 99%

A Study of Consumers’ Perceptions of Take-Out Food before and after the COVID-19 Outbreak: Applying Big Data Analysis

2022

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This study explored changes in consumers’ perceptions of take-out food before and after the onset of the COVID-19 pandemic using big data collected from social media. Using “take-out food” as a keyword, 18,544 search results were found in 2019, before the COVID-19 outbreak, compared to 20,718 search results in 2021. These keywords were analyzed using text mining, semantic network analysis, CONCOR analysis, and sentiment analysis, respectively, to understand consumers’ perception of take-out food. Using text mining, in 2019, “dining-out” was the most frequent search term associated with take-out food, followed by packing, famous restaurant, family, delicious, menu, and available. In 2021, “dining-out” was again the most popular keyword, followed by packing, famous restaurant, delivery, family, delicious, available, and Corona. A semantic network analysis showed that, in 2019, four categories emerged: delicious, meat, satisfaction, and lunchbox. The same analysis showed that, in 2021, the categories were delicious, meat, good, and home meal. These findings suggest that, after COVID-19, take-out food began to be recognized as a daily meal that can replace home-cooked meals. According to the sentiment analysis, the number of positive keywords decreased by 4.03% after COVID-19, while the number of negative keywords increased at the same rate; regarding the increase in negative keywords, such as sadness, disgust, and fear, since the emergence of COVID-19, consumers’ anxiety about eating out due to the virus was observed. This study can be useful by providing core data and an analysis method necessary for food service companies’ business activities and decision making related to take-out amid consumers’ rapidly changing needs for the dining-out environment caused by COVID-19.

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Section: Big Data In the Food Service Industrymentioning

confidence: 99%

A Study of Consumers’ Perceptions of Take-Out Food before and after the COVID-19 Outbreak: Applying Big Data Analysis

2022

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“…Sentiment analysis refers to a technique that classifies or quantifies emotions in text and turns them into objective information [49]. Humans use language to communicate their thoughts and feelings.…”

Section: Sentiment Analysismentioning

confidence: 99%

Topic Modeling and Sentiment Analysis of Online Review for Airlines

et al. 2021

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The purpose of this study is to conduct topic modeling and sentiment analysis on the posts of Skytrax (airlinequality.com), where there are many interests and participation of the people who have used or are willing to use it for airlines. The purpose of people gathering at Skytrax is to make better choices using the actual experiences of other customers who have experienced airlines. Online reviews written by customers with experience using airlines in Asia were collected. The data collected were online reviews from 27 airlines, with more than 14,000 reviews. Topic modeling and sentiment analysis were used with the collected data to figure out what kinds of important words are in the online reviews. As a result of the topic modeling, ‘seat’, ‘service’, and ‘meal’ were significant issues in the flight through frequency analysis. Additionally, the result revealed that delay was the main issue, which can affect customer dissatisfaction while ‘staff service’ can make customers satisfied through sentiment analysis as the result shows the ‘staff service’ with meal and food in the topic modeling.

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“…There are some studies that design emotional dictionaries using Korean antonyms [18] and emoticons, special symbols used in the Internet, and emotional symbols of Korean initials [19]. Liu, B et al [20] proposed a technique for summarizing product reviews using machine learning algorithms and natural language processing techniques. As a result, we have developed a system named Opinion Observer for research.…”

Section: Related Researchmentioning

confidence: 99%

Automated Product Review Collection and Opinion Analysis Methods for Efficient Business Analysis

Doo¹,

Shin²,

Park³

2021

IJCDS

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In this paper, we propose a business analysis method that automatically collects review information related to a specific product by using a web crawler, analyzes the customer's emotional response to the product, and supports marketing activities. In order to process data collected from web pages and social networks into information that can be used for marketing, several levels of data processing and text mining techniques are needed. Although various studies have been carried out for this purpose, the data collected through lots of effort and cost contain more extensive information than needed for marketing. So the usefulness of the information obtained through data processing and analysis is not really good. In this paper, we propose a system that automatically receives data from interested sites, interested fields, interested keywords, and target period information from a user and crawls the data. In addition, the reviews that can be used only in marketing can be selected to judge whether or not they are positive, thus it improves the accuracy of the analysis. For the experiment, we collected the reviews of the books sold by specific publishers over the past three years and conducted reputation analysis. The proposed classifier distinguishes through supervisor whether the data collected is a proper review or not. The accuracy of the review classifier is 98.7%. The reputation analyzer judges whether the review is positive or negative with the 86.1% accuracy. The results of this study can be used directly in various industries. And we plan to develop the reputation analyzer, improving the accuracy and extracting the reputation factors affecting customers.

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Automatic Construction of a Negative/positive Corpus and Emotional Classification using the Internet Emotional Sign

Cited by 11 publications

References 0 publications

A Study of Consumers’ Perceptions of Take-Out Food before and after the COVID-19 Outbreak: Applying Big Data Analysis

A Study of Consumers’ Perceptions of Take-Out Food before and after the COVID-19 Outbreak: Applying Big Data Analysis

Topic Modeling and Sentiment Analysis of Online Review for Airlines

Automated Product Review Collection and Opinion Analysis Methods for Efficient Business Analysis

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