• 한국비블리아학회지
• /
• 제7권1호
• /
• pp.247-262
• /
• 1994

This study is concerned with analysis of all the reviews published by the reviewing periodicals, The Book Review Culture and The Korean Publishing Journal, from 1991 to 1993. The result of analysis for 736 reviews are followed： 1) The percentage of reviews in the field of philosophy & religion, literature ＆ language, science ＆ technology is lower than the percent-age of books published. But in the field of history and social science the reviewing is proportionately higher than the publishing. 2) Book reviews are prepared by professors, literary reviewers, researchers, and experts in the particular subject field except librarian. 3) Basic elements of reviewing are the career and view point of author, trends of suject field, content, value, omissions, limitations, and format of book, reader's level, etc. Ideal method of book criticism may be summarized as follows： 1) The criterion of book selection are the book's value, the social . demand, and the proportion of titles published. 2) For the unbiased criticism, it should be written by the experienced librarian rather than the experts of particular subject field. 3) Book criticism need to provide not only guide to new books but also interpretation and evaluation about each book for its reader.

• Journal of Information Processing Systems
• /
• 제16권3호
• /
• pp.718-732
• /
• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.

• 디지털산업정보학회논문지
• /
• 제15권1호
• /
• pp.87-97
• /
• 2019

Reaction of people is importantly considered about specific case as a social network service grows. In the previous research on analysis of social network service, they predicted tendency of interesting topic by giving scores to sentences written by user. Based on previous study we proceeded research of sentiment analysis for social network service's sentences, which predict the result as positive or negative for movie reviews. In this study, we used movie review to get high accuracy. We classify the movie review into positive or negative based on the score for learning. Also, we performed embedding and morpheme analysis on movie review. We could predict learning result as positive or negative with a number 0 and 1 by applying the model based on learning result to social network service. Experimental result show accuracy of about 80% in predicting sentence as positive or negative.

• Journal of Multimedia Information System
• /
• 제9권1호
• /
• pp.61-68
• /
• 2022

The importance of online reviews is prevalent as more people access goods or places online and make decisions to visit or purchase. However, such reviews are generally provided by short sentences or mere star ratings; failing to provide a general overview of customer preferences and decision factors. This study explored and broke down restaurant reviews found on Google Maps. After collecting and analyzing 5,427 reviews, we vectorized the importance of words using the TF-IDF. We used a random forest machine learning algorithm to calculate the coefficient of positivity and negativity of words used in reviews. As the result, we were able to build a dictionary of words for positive and negative sentiment using each word's coefficient. We classified words into four major evaluation categories and derived insights into sentiment in each criterion. We believe the dictionary of review words and analyzing the major evaluation categories can help prospective restaurant visitors to read between the lines on restaurant reviews found on the Web.

• 패션비즈니스
• /
• 제20권4호
• /
• pp.207-225
• /
• 2016

This study aims to analyze the impact of negative review type, brand reputation and opportunity scarcity perception, on preferences of fashion products in social commerce. For the above evaluation, we used the 2 (negative review type: objective/subjective) ${\times}2$ (brand reputation: high/low) ${\times}2$ (opportunity scarcity perception: high/low) model, designed with three mixed elements. We enrolled 260 women in their 20s and 30s, who live in Seoul and have used social commerce; a final total of 207 subjects were considered for analysis. The data were analyzed using the SPSS 18 program and reliability test, t-test and three-way ANOVA were performed. Following observations were made: First, preferences were higher when the subjects read objective negative reviews than subjective negative reviews, and when a fashion product was from a brand of high reputation than a brand of low reputation. Second, the interaction effect between negative review type and brand reputation was greater among the subjects whose opportunity scarcity perception is high, than those having low opportunity scarcity perception. Thus, we conclude that the social commerce should encourage consumers to write more objective reviews, and fashion brands should manage their reputations well. Also, social commerce can use scarcity messages aggressively to increase preferences of global fashion luxury goods, which is actively marketed in social commerce since 2015.

• 지능정보연구
• /
• 제22권1호
• /
• pp.63-82
• /
• 2016

최근 정보기술의 발달로 인해 소비자들은 온라인상에서 많은 정보를 쉽고 빠르게 획득할 수 있다. 소비자가 제품 구매시에는 소비자들이나 전문가들이 작성한 제품 후기 정보를 주로 탐색한다. 기존의 연구들이 소비자들이 창출한 제품 후기 중심으로 주로 진행되어 왔기 때문에, 전문가 제품 후기의 영향력에 대해서는 상대적으로 소수의 연구들만 존재하고 있다. 본 연구는 전문가가 생성하는 제품 후기에 초점을 맞추어, 방대한 실제 비정형데이터인 전문가의 후기를 어떻게 언어학적인 차원과 심리학적인 차원으로 나눌 수 있는지의 방법론을 제안하며, 실제 전문가 제품 후기를 사용하여 의미 있는 다섯 가지 차원의 새로운 변수들을 도출하였다. 그 결과 소비자들이 전문가 후기에서 반응하고 있는 언어적 특성은 제품에 대한 깊이 있는 정보의 양이나 충분한 설명을 나타내는 변수인 Review Depth, 그리고 전문가가 기술하는 방식이 제품에 대한 확신이 없는 듯한 말투를 나타내는 변수인 Lack of Assurance는 소비자의 전반적인 제품평가에 유의한 상관관계가 있는 것으로 밝혀졌다. 또한, 제품에 대한 칭찬이나 긍정적인 면을 서술하는 방식인 Positive Polarity가 소비자의 제품 평가에 영향을 미치지 않았지만, 전문가가 하는 제품에 대한 비관적인 평가인 Negative Polarity는 소비자들의 평가와 유의한 음의 상관관계가 있었다는 점이다. 전문가가 스토리텔링 관점에서 자주 사용하는 Social Orientation 특성은 유의한 관계를 미치지 못함이 밝혀졌다. 본 연구는 새로운 방법론을 제안하고 이를 실제로 활용한 결과를 보여준다는 차원에서 이론적이고 실무적인 공헌을 가진다.

• 정보화정책
• /
• 제20권3호
• /
• pp.3-17
• /
• 2013

온라인 상점의 발전과 뉴미디어의 등장으로 소비자들 사이에 상품정보에 대한 의견이 교환되기 시작하면서 온라인 소비자 리뷰에 대한 많은 연구가 진행되고 있다. 이에 본 연구에서는 온라인 소비자 리뷰와 관련된 연구의 개괄적인 흐름을 파악하여 연구경향을 분석하고 향후 연구방향을 탐색하였다. 온라인 소비자 리뷰 연구에서는 리뷰의 유용성, 리뷰로 인한 소비자의 태도변화를 주로 다루었으며 이에 영향을 미치는 변수들은 대체로 메시지 요인, 리뷰어 요인, 소비자 요인, 그리고 제품/서비스 요인으로 구분되었다. 메시지의 특성에 대한 연구는 메시지의 양과 평가적 메시지가 많을수록 유용하며, 메시지의 방향성에 대해서는 상황에 따라 다르게 나타나고 있다. 또한 리뷰어의 신뢰성 및 평판, 소비자의 제품지식과 제품관여도, 제품/서비스의 유형에 의해 리뷰의 영향력이 어떻게 달라지는지에 대한 연구가 이루어지고 있다. 향후에는 소셜미디어의 등장으로 보다 활발하게 소비자 리뷰가 이루어지고 사회적 연결망을 통해 전달됨에 따라 이에 따른 소비자 리뷰의 영향력에 대한 연구가 추가적으로 이루어질 필요가 있다. 또한 온라인 소비자 리뷰를 역이용하는 기업이 늘어남에 따라 온라인 소비자 리뷰의 부작용에 대한 실증적인 연구가 필요해 보인다.

• 한국사회복지학
• /
• 제43권
• /
• pp.328-357
• /
• 2000

From Foucault's Perspective of power, this study is trying to illuminate the characteristics and limitations of 'empowerment' which is widely accepted as a central value and practice skill of social work. Notwithstanding the superficial consensus on the empowerment, the author shows that it is a confusing concept with contrasting expectations and conflicting methodologies or only a wishful rhetorical jargon. Furthermore, he argues that the empowerment is not just a value-free intervention skill working outside the ruling power but a ruling-discourse or power-mechanism of a liberal society which makes citizens responsible voluntarily. For a theoretical background for these arguments, the 2nd chapter reviews Foucault's theory of power. The 1st part of the 3rd chapter summarizes the historical background of empowerment practice and its methodological characteristics and meanings, the 2nd part reviews the existing critics on the conceptual and practical limitations of empowerment, and the last part reveals, based upon Foucault's theory of power, that the empowerment is a typical mode of ruling power in liberal societies. The author expects that this study may warn the moral and intellectual superiority complex of social work discourse and help stimulate the ethical sensibility and responsibility in social work practice.

• International Journal of Computer Science & Network Security
• /
• 제21권8호
• /
• pp.238-246
• /
• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• The Journal of Asian Finance, Economics and Business
• /
• 제8권8호
• /
• pp.443-451
• /
• 2021

The classification standards for hotels in Vietnam are different from many other hotel classification standards in the world. This study aims to analyze customer reviews on the TripAdvisor website to develop a new algorithm for hotel rating that is independent of Vietnam's hotel classification standards. This method can be applied to individual hotels, or hotels of a region or the whole country, while online booking sites only rate individual hotels. Data was crawled from TripAdvisor with 22,287 reviews of 5 cities in Vietnam. This study used a statistical model to analyze the review dataset and build an algorithm to rate hotels according to aspects or hotel overall. The results have less rating deviation when compared to the TripAdvisor system. This study also supports hotel managers to regularly update the status of their hotels using data from customer reviews, from which, managers can strategize long-term solutions to improve the quality of the hotel in all aspects and attract more travelers to Vietnam. Moreover, this method can be developed into an automatic system to rate hotels and update the status of service quality more quickly, thus, saving time and costs.