Automatic labeling of mobile apps by the type of psychological needs they satisfy

Sun, Zaoyi; Z, Ji; Zhang, Pei; Chen, Chuansheng; Qian, Xiuying; Du, Xin; Wan, Qun

doi:10.1016/j.tele.2017.03.001

Cited by 17 publications

(31 citation statements)

References 30 publications

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“…In fact, grounded theory is widely used in research about emotions [79,80] and other areas, such as personality [81], need [82], and psychological resilience [83]. A combination of qualitative research and artificial intelligence methods has also been advocated in recent years [45]. Therefore, in Study 2, we used a semiautomated method that combines the results of grounded theory, natural language processing (NLP), and manual screening to build the lexicon.…”

Section: General Discussion and Conclusionmentioning

confidence: 99%

“…Considering the above issues, we advocated an emotion lexicon construction method as follows: (a) using grounded theory approach to build a theoretical model based on the target context (Weibo in this study); (b) using the dimension of the model as the basis for the emotion category and extracting seed words based on the category; and (c) using machine learning algorithms to expand the emotion lexicon and manually filter the expanded results. This method is an extension of our previous work of corpus construction [45].…”

Section: B Construction Of An Emotion Lexiconmentioning

confidence: 96%

“…In this part, substantive coding, including open, axial, and selective coding [45], was first conducted to build the preliminary theoretical model of users' emotional expression on Weibo, and a total of 6,633 texts (88.03%) were coded in this step. Then, the remaining 902 texts were used for the theoretical saturation test.…”

Section: B Data Coding and Analysismentioning

confidence: 99%

“…In this part, our goal was to build a seed word set for each emotion category. Referring to the method of [45], we used 597 nodes, 46 main categories, and 8 core categories, all of which were identified by using grounded theory in Study 1 as original words. Then, we manually checked these words to remove ambiguous words and antonyms.…”

Section: A Construction Of Seed Word Setmentioning

confidence: 99%

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A Novel Emotion Lexicon for Chinese Emotional Expression Analysis on Weibo: Using Grounded Theory and Semi-Automatic Methods

Jiang

et al. 2021

IEEE Access

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As one of the most popular social media platforms in China, Weibo has aggregated huge numbers of texts containing people's thoughts, feelings, and experiences. Analyzing emotions expressed on Weibo has attracted a great deal of academic attention. Emotion lexicon is a vital foundation of sentiment analysis, but the existing lexicons still have defects such as a limited variety of emotions, poor crossscenario adaptability, and confusing written and online expressions and words. By combining grounded theory and semi-automatic methods, we built a Weibo-based emotion lexicon for sentiment analysis. We first took a bottom-up approach to derive a theoretical model for emotions expressed on Weibo, and the substantive coding led to eight core emotion categories: joy, expectation, love, anger, anxiety, disgust, sadness, and surprise. Second, we built a new emotion lexicon containing 2,964 words by manually selecting seed words, constructing a word vector model to expand words, and making rules to filter words. Finally, we tested the effectiveness of our lexicon by using a lexicon-based approach to recognize the emotions expressed in Weibo text. The results showed that our lexicon performed better in Weibo emotion recognition than five other Chinese emotion lexicons. This study proposed a method to construct an emotion lexicon that considered both theory and application by combining qualitative research and artificial intelligence methods. Our work also provided a reference for future research in the field of social media sentiment analysis.

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Section: General Discussion and Conclusionmentioning

confidence: 99%

Section: B Construction Of An Emotion Lexiconmentioning

confidence: 96%

Section: B Data Coding and Analysismentioning

confidence: 99%

Section: A Construction Of Seed Word Setmentioning

confidence: 99%

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A Novel Emotion Lexicon for Chinese Emotional Expression Analysis on Weibo: Using Grounded Theory and Semi-Automatic Methods

Jiang

et al. 2021

IEEE Access

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“…The current study is an extension of a previous study that developed a method to automatically label mobile Apps in terms of whether and to what extent they can satisfy users’ particular psychological needs [21]. In that study, the researchers obtained in-depth interview data from App users about what needs were satisfied by the Apps they used and crawled the App stores for user reviews for similar information.…”

Section: Introductionmentioning

confidence: 99%

Cross-situation consistency of mobile App users’ psychological needs

Sun

Zhang

et al. 2019

PLoS ONE

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Previous studies showed that individuals’ traits could be used to explain the similarity of behavioral patterns across different occasions. Such studies have typically focused on personality traits, and have not been extended to psychological needs. Our study used a large dataset of 1,715,078 anonymous users’ App usage records to examine whether the individual’s needs-based profiles of App usage were consistent across different situations (as indexed by categories of App functions). Results showed a high level of consistency across situations in a user’s choice of Apps based on the needs the Apps could satisfy. These results provide clear evidence in support of cross-category App recommendation systems.

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Habib

Marimuthu

2019

Advances in Intelligent Systems and Computing

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Automatic labeling of mobile apps by the type of psychological needs they satisfy

Cited by 17 publications

References 30 publications

A Novel Emotion Lexicon for Chinese Emotional Expression Analysis on Weibo: Using Grounded Theory and Semi-Automatic Methods

A Novel Emotion Lexicon for Chinese Emotional Expression Analysis on Weibo: Using Grounded Theory and Semi-Automatic Methods

Cross-situation consistency of mobile App users’ psychological needs

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