Emotion Expression Extraction Method for Chinese Microblog Sentences

With the rapid spread of Chinese microblog, a large number of microblog topics are being generated in real-time. More and more users pay attention to emotion expressions of these opinionated sentences in different topics. It is challenging to label the emotion expressions of opinionated sentences manually. For this endeavor, an emotion expression extraction method is proposed to process millions of user-generated opinionated sentences automatically in this paper. Specifically, the proposed method mainly contains two tasks: emotion classification and opinion target extraction. We first use a lexicon-based emotion classification method to compute different emotion values in emotion label vectors of opinionated sentences. Then emotion label vectors of opinionated sentences are revised by an unsupervised emotion label propagation algorithm. After extracting candidate opinion targets of opinionated sentences, the opinion target extraction task is performed on a random walk-based ranking algorithm, which considers the connection between candidate opinion targets and the textual similarity between opinionated sentences, ranks candidate opinion targets of opinionated sentences. Experimental results demonstrate the effectiveness of algorithms in the proposed method

MORE THAN THE TONE: THE IMPACT OF SOCIAL MEDIA OPINIONS ON INNOVATION INVESTMENTS

Social media is a valuable knowledge source for firm innovation. Extending the literature of both social media and innovation management, we attempt to examine how the valence and volume of user-generated content (UGC) from social media influence firm organizational innovation behav-iours. In this research-in-progress study, we have reviewed the existing literatures and proposed three hypotheses. Firstly, we propose that valence of UGC from social media has a U-shaped rela-tion with firm innovation investments. In particular, compared with neutral UGC, both negative and positive contents are found to push firms to invest more in innovation. Secondly, we argued that such a curvilinear relation is mitigated with an increase in volume of UGC. Last but not least, we argued that firm investment in innovation improves firm performance. To validate our pro-posed hypotheses, we have designed an innovative framework of sentiment analysis and collected a large dataset including 5-year panel with 886 listed firms and their relevant 6.2 million micro-blogs. The preliminary results from applying sentiment analysis into the collected dataset are re-ported in this study. In the future, we will validate our hypotheses with more sophisticated estima-tion models and strict robustness check. The potential contribution to theory and practice is also discussed

Cosine similarity-based algorithm for social networking recommendation

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities

FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT

Automatic emotion detection in text is concerned with using natural language processing techniques to recognize emotions expressed in written discourse. Endowing computers with the ability to recognize emotions in a particular kind of text, microblogs, has important applications in sentiment analysis and affective computing. In order to build computational models that can recognize the emotions represented in tweets we need to identify a set of suitable emotion categories. Prior work has mainly focused on building computational models for only a small set of six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). This thesis describes a taxonomy of 28 emotion categories, an expansion of these six basic emotions, developed inductively from data. This set of 28 emotion categories represents a set of fine-grained emotion categories that are representative of the range of emotions expressed in tweets, microblog posts on Twitter. The ability of humans to recognize these fine-grained emotion categories is characterized using inter-annotator reliability measures based on annotations provided by expert and novice annotators. A set of 15,553 human-annotated tweets form a gold standard corpus, EmoTweet-28. For each emotion category, we have extracted a set of linguistic cues (i.e., punctuation marks, emoticons, emojis, abbreviated forms, interjections, lemmas, hashtags and collocations) that can serve as salient indicators for that emotion category. We evaluated the performance of automatic classification techniques on the set of 28 emotion categories through a series of experiments using several classifier and feature combinations. Our results shows that it is feasible to extend machine learning classification to fine-grained emotion detection in tweets (i.e., as many as 28 emotion categories) with results that are comparable to state-of-the-art classifiers that detect six to eight basic emotions in text. Classifiers using features extracted from the linguistic cues associated with each category equal or better the performance of conventional corpus-based and lexicon-based features for fine-grained emotion classification. This thesis makes an important theoretical contribution in the development of a taxonomy of emotion in text. In addition, this research also makes several practical contributions, particularly in the creation of language resources (i.e., corpus and lexicon) and machine learning models for fine-grained emotion detection in text

The Roles of Culture in Online User Reviews: An Empirical Investigation

Electronic word-of-mouth (eWOM) is a prominent source of information that significantly influences consumer purchase decisions. Recent literature has extensively explored the impact of eWOM on consumers-generated reviews and purchase decisions. However, few studies have analyzed the role of culture on eWOM. We use a novel dataset of Airbnb eWOM messages in order to empirically extend the findings by Banerjee and Chai (2019). We find that the sentiment of individualistic customers is worse than that of their collectivistic counterparts when both groups experience the same level of negative disconfirmations. Furthermore, guests from a relatively more distant culture rely less on heuristics. In particular, quality signals, such as the "superhost" status, are more influential to consumers from a less distant cultural background.Comment: 35 pages, 4 tables, 2 figure

Context-Preserving Visual Analytics of Multi-Scale Spatial Aggregation.

Spatial datasets (i.e., location-based social media, crime incident reports, and demographic data) often exhibit varied distribution patterns at multiple spatial scales. Examining these patterns across different scales enhances the understanding from global to local perspectives and offers new insights into the nature of various spatial phenomena. Conventional navigation techniques in such multi-scale data-rich spaces are often inefficient, require users to choose between an overview or detailed information, and do not support identifying spatial patterns at varying scales. In this work, we present a context-preserving visual analytics technique that aggregates spatial datasets into hierarchical clusters and visualizes the multi-scale aggregates in a single visual space. We design a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates and explore visual encoding strategies including color, transparency, shading, and shapes, in order to illustrate the hierarchical and statistical patterns of the multi-scale aggregates. We also propose a transparency-based technique that maintains a smooth visual transition as the users navigate across adjacent scales. To further support effective semantic exploration in the multi-scale space, we design a set of text-based encoding and layout methods that draw textual labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each scale, but also indicates the spatial coverage of the aggregates and their hierarchical relationships. We demonstrate the effectiveness of the proposed approaches through real-world application examples and user studies

Twitter Sentiment Analysis

Social media continues to gain increased presence and importance in society. Public and private opinion about a wide variety of subjects are expressed and spread continually via numerous social media. Twitter is one of the social media that is gaining increased popular. Twitter offers organizations a fast and effective way to analyze customers‟ perspectives toward the critical to success in the marketplace. Developing a program for sentiment analysis is an approach to be used to computationally measure customers‟ perceptions. This paper reports on the design of a sentiment analysis extracting a vast amount of tweets. Prototyping is used in this development. Results classify customers‟ perspective via tweets into positive and negative which is represented in pie chart and html page. However, the program has planned to develop on web application system but due to limitation of Django which can be worked on Linux server or LAMP, for further this approach need to be done