Personalized Recommendation Model: An Online Comment Sentiment Based Analysis

Chen, Songmin; Lv, Xiyan; Gou, Juanqiong

doi:10.15837/ijccc.2020.1.3764

Cited by 17 publications

(10 citation statements)

References 21 publications

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“…When people communicate, they habitually use negative words and adverbs of degree. Although negative words and adverbs of degree do not have emotional polarity, when they are combined with emotional phrases, they will affect the original emotional tendency of emotional words [ 39 ]. Specifically, the combination of degree adverb plus emotional words will enhance or weaken the original emotional tendency of emotional words, such as “really fancy”.…”

Section: Methodsmentioning

confidence: 99%

Sentiment Classification Method Based on Blending of Emoticons and Short Texts

Zou

Xiang

2022

Entropy

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With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. Short text reduces the threshold of information production and reading by virtue of its short length, which is in line with the trend of fragmented reading in the context of the current fast-paced life. In addition, short texts contain emojis to make the communication immersive. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. Emoticons and short-text content are transformed into vectors, and the corresponding word vector and emoticon vector are connected into a sentencing matrix in turn. The sentence matrix is input into a convolution neural network classification model for classification. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.

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Section: Methodsmentioning

confidence: 99%

Sentiment Classification Method Based on Blending of Emoticons and Short Texts

Zou

Xiang

2022

Entropy

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“…The second step is word frequency analysis. The purpose of word frequency analysis is to understand the characteristic word frequency and quickly grasp the comment hot spots in this situation ( Chen et al, 2020 ). The nouns and adjectives after word segmentation are arranged in descending order.…”

Section: Positive User Experience Design Process Based On the Product...mentioning

confidence: 99%

Exploring the Positive User Experience Possibilities Based on Product Emotion Theory: A Beverage Unmanned Retail Terminal Case

et al. 2022

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Since the last century, user experience has been regarded as a key concept in the process of product and service design. With the development of positive psychology, the transformation from negative to positive user experience has also taken place in the field of user experience; it emphasizes exploring the future possibility of positive user experience rather than just solving existing problems. Based on the research and analysis of existing literature, this study makes it clear that positive user experience research should be based on the “positive experience,” and arousing a positive emotion is conducive to improving positive user experience. On this basis, the product emotion theory is applied to the analysis process of “positive experience.” Through word frequency screening, thematic analysis, and correlation calculation, the relationship between product stimulus (object, activity, and identity) and user concern (goal, attitude, and standard) based on positive “user comments” is constructed, and positive user experience is understood from multiple levels. Based on the comment score, the positive user experience interval is divided in order to clarify the improvement direction. Finally, taking the “Angel Orange” unmanned retail terminal as an example, this study carried out an empirical analysis. As an exploratory study, this study can provide some insights into the quantitative research process of positive user experience design that evokes positive emotions from a user’s “positive experience” story.

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“…[1] propose a deep learning network for sentiment analysis. There are also some research and application of sentiment analysis, such as Sánchez et al [4] introduce an open-source service framework for sentiment analysis, Huddar et al [5] propose a multi-level feature optimization model for sentiment analysis, Chen et al [7] propose a personalized recommendation model based on sentiment analysis, Machová et al [8] study sentiment analysis in conversation content.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of News Reports in the Period of New Coronavirus

Li¹,

Pan²

2021

dtcse

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The spread of new coronavirus has a significant impact on the world. To protect people from viruses, a large number of personnel and substances in research, production, medical treatment, and other aspects are used to deal with the virus. News reporting is an important way for people to obtain information about the coronavirus. Objective and immediate news reports can provide people with accurate information and correct response methods, thus playing their due role in defeating the coronavirus. In order to compare the emotional characteristics of news reports, the methods of natural language processing are used to conduct sentiment analysis on news reports, including the subjectivity and the polarity of news reports. We select the news websites of BBC and CNN, that have an important influence in the world, for comparative analysis. The results show that BBC news reports are more positive than CNN, and in terms of subjective, BBC News is more factual than CNN.

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Personalized Recommendation Model: An Online Comment Sentiment Based Analysis

Cited by 17 publications

References 21 publications

Sentiment Classification Method Based on Blending of Emoticons and Short Texts

Sentiment Classification Method Based on Blending of Emoticons and Short Texts

Exploring the Positive User Experience Possibilities Based on Product Emotion Theory: A Beverage Unmanned Retail Terminal Case

Sentiment Analysis of News Reports in the Period of New Coronavirus

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