Social media platforms such as Twitter and the Internet Movie Database (IMDb) contain a vast amount of data which have applications in predictive sentiment analysis for movie sales, stock market fluctuations, brand op...Social media platforms such as Twitter and the Internet Movie Database (IMDb) contain a vast amount of data which have applications in predictive sentiment analysis for movie sales, stock market fluctuations, brand opinion, or current events. Using a dataset taken from IMDb by Stanford, we identify some of the most significant phrases for identifying sentiment in a wide variety of movie reviews. Data from Twitter are especially attractive due to Twitter’s real-time nature through its streaming API. Effectively analyzing this data in a streaming fashion requires efficient models, which may be improved by reducing the dimensionality of input vectors. One way this has been done in the past is by using emoticons;we propose a method for further reducing these features through identifying common structure in emoticons with similar sentiment. We also examine the gender distribution of emoticon usage, finding tendencies towards certain emoticons to be disproportionate between males and females. Despite the roughly equal gender distribution on Twitter, emoticon usage is predominately female. Furthermore, we find that distributed vector representations, such as those produced by Word2Vec, may be reduced through feature selection. This analysis was done on a manually labeled sample of 1000 tweets from a new dataset, the Large Emoticon Corpus, which consisted of about 8.5 million tweets containing emoticons and was collecting over a five day period in May 2015. Additionally, using the common structure of similar emoticons, we are able to characterize positive and negative emoticons using two regular expressions which account for over 90% of emoticon usage in the Large Emoticon Corpus.展开更多
Nowadays the frequent and prevalent employment of internet emoticon has attracted people's attention of it. This paper is to comb the developmental history of internet emoticon and endeavor to give some hints on i...Nowadays the frequent and prevalent employment of internet emoticon has attracted people's attention of it. This paper is to comb the developmental history of internet emoticon and endeavor to give some hints on its future research. In this review, 88 papers are collected from the database of China National Knowledge Infrastructure(CNKI). A visualization analysis is made to quantify the research hot topics and frontier, key words, author and institution with the aid of CiteSpace III. In terms of cited literature and co-cited literature, CNKI metrological visualization analysis is employed to delve into the overall trends. The results reveal that internet emoticon research has been closely related to various domains and its interdisciplinary characteristics is not in full swing.展开更多
User experience is understood in so many ways,like a one on one interaction(subjective views),online surveys and questionnaires.This is simply so get the user’s implicit response,this paper demonstrates the underlyin...User experience is understood in so many ways,like a one on one interaction(subjective views),online surveys and questionnaires.This is simply so get the user’s implicit response,this paper demonstrates the underlying user emotion on a particular interface such as the webpage visual content based on the context of familiarisation to convey users’emotion on the interface using emoji,we integrated physiological readings and eye movement behaviour to convey user emotion on the visual centre field of a web interface.The physiological reading is synchronised with the eye tracker to obtain correlating user interaction,and emoticons are used as a form of emotion conveyance on the interface.The eye movement prediction is obtained through a control system’s loop and is represented by different color display of gaze points(GT)that detects a particular user’s emotion on the webpage interface.These are interpreted by the emoticons.Result shows synchronised readings which correlates to area of interests(AOI)of the webpage and user emotion.These are prototypical instances of authentic user response execution for a computer interface and to easily identify user response without user subjective response for better and easy design decisions.展开更多
Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analy...Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model （ESM） that leverages more emotieons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus （NLP＆CC 2013） indicate that ESM effectively leverages emoticon signals best runs. and outperforms previous state-of-the-art strategies and benchmark展开更多
文摘Social media platforms such as Twitter and the Internet Movie Database (IMDb) contain a vast amount of data which have applications in predictive sentiment analysis for movie sales, stock market fluctuations, brand opinion, or current events. Using a dataset taken from IMDb by Stanford, we identify some of the most significant phrases for identifying sentiment in a wide variety of movie reviews. Data from Twitter are especially attractive due to Twitter’s real-time nature through its streaming API. Effectively analyzing this data in a streaming fashion requires efficient models, which may be improved by reducing the dimensionality of input vectors. One way this has been done in the past is by using emoticons;we propose a method for further reducing these features through identifying common structure in emoticons with similar sentiment. We also examine the gender distribution of emoticon usage, finding tendencies towards certain emoticons to be disproportionate between males and females. Despite the roughly equal gender distribution on Twitter, emoticon usage is predominately female. Furthermore, we find that distributed vector representations, such as those produced by Word2Vec, may be reduced through feature selection. This analysis was done on a manually labeled sample of 1000 tweets from a new dataset, the Large Emoticon Corpus, which consisted of about 8.5 million tweets containing emoticons and was collecting over a five day period in May 2015. Additionally, using the common structure of similar emoticons, we are able to characterize positive and negative emoticons using two regular expressions which account for over 90% of emoticon usage in the Large Emoticon Corpus.
文摘Nowadays the frequent and prevalent employment of internet emoticon has attracted people's attention of it. This paper is to comb the developmental history of internet emoticon and endeavor to give some hints on its future research. In this review, 88 papers are collected from the database of China National Knowledge Infrastructure(CNKI). A visualization analysis is made to quantify the research hot topics and frontier, key words, author and institution with the aid of CiteSpace III. In terms of cited literature and co-cited literature, CNKI metrological visualization analysis is employed to delve into the overall trends. The results reveal that internet emoticon research has been closely related to various domains and its interdisciplinary characteristics is not in full swing.
文摘User experience is understood in so many ways,like a one on one interaction(subjective views),online surveys and questionnaires.This is simply so get the user’s implicit response,this paper demonstrates the underlying user emotion on a particular interface such as the webpage visual content based on the context of familiarisation to convey users’emotion on the interface using emoji,we integrated physiological readings and eye movement behaviour to convey user emotion on the visual centre field of a web interface.The physiological reading is synchronised with the eye tracker to obtain correlating user interaction,and emoticons are used as a form of emotion conveyance on the interface.The eye movement prediction is obtained through a control system’s loop and is represented by different color display of gaze points(GT)that detects a particular user’s emotion on the webpage interface.These are interpreted by the emoticons.Result shows synchronised readings which correlates to area of interests(AOI)of the webpage and user emotion.These are prototypical instances of authentic user response execution for a computer interface and to easily identify user response without user subjective response for better and easy design decisions.
基金Tsinghua-Samsung Joint Laboratory, the National Basic Research 973 Program of China under Grant No. 2015CB358700, and the National Natural Science Foundation of China under Grant Nos. 61472206, 61073071, and 61303075.
文摘Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model （ESM） that leverages more emotieons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus （NLP＆CC 2013） indicate that ESM effectively leverages emoticon signals best runs. and outperforms previous state-of-the-art strategies and benchmark
