Role of Emoticons in Sentence-Level Sentiment Classification

Author(s):  
Martin Min ◽  
Tanya Lee ◽  
Ray Hsu
2015 ◽  
Vol 23 (11) ◽  
pp. 1750-1761 ◽  
Author(s):  
Duyu Tang ◽  
Bing Qin ◽  
Furu Wei ◽  
Li Dong ◽  
Ting Liu ◽  
...  

2020 ◽  
Author(s):  
Manyu Li

This secondary-analysis register report aims at testing the role of emotion in the intervention effect of an experimental intervention study in academic settings. Previous analyses of the National Study of the Learning Mindset (Yeager et al., 2019) showed that in a randomized controlled trial, high school students who were given the growth mindset intervention had, on average higher GPA than did students in the control condition. Previous analyses also showed that school achievement levels moderated the intervention effect. This study further explores whether the emotion students experienced during the growth mindset intervention plays a role in the intervention effect. Specifically, using a sentence-level automated text analysis for emotional valence (i.e. sentiment analysis), students’ written reflections during the intervention are analyzed. Linear mixed models are conducted to test if valence reflected in the written texts predicted higher intervention effect (i.e. higher post-intervention GPA given pre-intervention GPA). The moderating role of school achievement levels was also examined. A 10% random sample of the data was analyzed as a pilot study for this registered report to test for feasibility and proof-of-concept. Results of the pilot data showed small, yet significant relations between emotional valence and intervention effects. The results of this study have implications on the role of emotion in the results of intervention or experimental studies, especially those that are conducted in academic settings. This study also introduces a user-friendly text-based analytic method for experimental psychologists to detect and analyze sentence-level emotional valence in an intervention or experimental study.


PLoS ONE ◽  
2016 ◽  
Vol 11 (5) ◽  
pp. e0155036 ◽  
Author(s):  
Igor Mozetič ◽  
Miha Grčar ◽  
Jasmina Smailović

2019 ◽  
Vol 9 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Nicola Burns ◽  
Yaxin Bi ◽  
Hui Wang ◽  
Terry Anderson

There is a need to automatically classify information from online reviews. Customers want to know useful information about different aspects of a product or service and also the sentiment expressed towards each aspect. This article proposes an Enhanced Twofold-LDA model (Latent Dirichlet Allocation), in which one LDA is used for aspect assignment and another is used for sentiment classification, aiming to automatically determine aspect and sentiment. The enhanced model incorporates domain knowledge (i.e., seed words) to produce more focused topics and has the ability to handle two aspects in at the sentence level simultaneously. The experiment results show that the Enhanced Twofold-LDA model is able to produce topics more related to aspects in comparison to the state of arts method ASUM (Aspect and Sentiment Unification Model), whereas comparable with ASUM on sentiment classification performance.


2019 ◽  
Author(s):  
Hao Wang ◽  
Bing Liu ◽  
Chaozhuo Li ◽  
Yan Yang ◽  
Tianrui Li

Author(s):  
Brandi Jett ◽  
Emily Buss ◽  
Virginia Best ◽  
Jacob Oleson ◽  
Lauren Calandruccio

Purpose Three experiments were conducted to better understand the role of between-word coarticulation in masked speech recognition. Specifically, we explored whether naturally coarticulated sentences supported better masked speech recognition as compared to sentences derived from individually spoken concatenated words. We hypothesized that sentence recognition thresholds (SRTs) would be similar for coarticulated and concatenated sentences in a noise masker but would be better for coarticulated sentences in a speech masker. Method Sixty young adults participated ( n = 20 per experiment). An adaptive tracking procedure was used to estimate SRTs in the presence of noise or two-talker speech maskers. Targets in Experiments 1 and 2 were matrix-style sentences, while targets in Experiment 3 were semantically meaningful sentences. All experiments included coarticulated and concatenated targets; Experiments 2 and 3 included a third target type, concatenated keyword-intensity–matched (KIM) sentences, in which the words were concatenated but individually scaled to replicate the intensity contours of the coarticulated sentences. Results Regression analyses evaluated the main effects of target type, masker type, and their interaction. Across all three experiments, effects of target type were small (< 2 dB). In Experiment 1, SRTs were slightly poorer for coarticulated than concatenated sentences. In Experiment 2, coarticulation facilitated speech recognition compared to the concatenated KIM condition. When listeners had access to semantic context (Experiment 3), a coarticulation benefit was observed in noise but not in the speech masker. Conclusions Overall, differences between SRTs for sentences with and without between-word coarticulation were small. Beneficial effects of coarticulation were only observed relative to the concatenated KIM targets; for unscaled concatenated targets, it appeared that consistent audibility across the sentence offsets any benefit of coarticulation. Contrary to our hypothesis, effects of coarticulation generally were not more pronounced in speech maskers than in noise maskers.


Sign in / Sign up

Export Citation Format

Share Document