Emotion Analysis
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2022 ◽  
Vol 2022 ◽  
pp. 1-12
Ran Li ◽  
Yuanfei Zhang ◽  
Lihua Yin ◽  
Zhe Sun ◽  
Zheng Lin ◽  

Emotion lexicon is an important auxiliary resource for text emotion analysis. Previous works mainly focused on positive and negative classification and less on fine-grained emotion classification. Researchers use lexicon-based methods to find that patients with depression express more negative emotions on social media. Emotional characteristics are an effective feature in detecting depression, but the traditional emotion lexicon has limitations in detecting depression and ignores many depression words. Therefore, we build an emotion lexicon for depression to further study the differences between healthy users and patients with depression. The experimental results show that the depression lexicon constructed in this paper is effective and has a better effect of classifying users with depression.

Swati Sahu

Abstract: As we know after covid-19 usage of Gym and yoga center is really difficult so most of us using the online gym and yoga session , where we use multiple fitness equipment but some time we use those fitness equipment in wrong direction which causes serious health issues in future to avoid those issues there is need of smart fitness equipment which is able to give the suggestion once we use the equipment in wrong direction. , in this paper basically we did the study about the previous existing work on emotion analysis and try to find out the research gaps and there future scope. Keywords: IoT, Smart, Fitness, Health, Connectivity, BLE

Rewati Saha

Abstract: As we know we are living in the era of digital world where everything is based on data analysis, now a days after covid it’s really difficult to do the analysis on real world, so there is need of an algorithm which is able to do the analysis on virtual world, suppose there is any application which is able to identify the user feedback based on there emotion, so there is need of a novel algorithm which is work on the concept of the emotion analysis, in this paper basically we did the study about the previous existing work on emotion analysis and try to find out the research gaps and there future scope. Keywords: Computer Vision, Machine Learning, Emotion Analysis, Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Wei Wan ◽  
Yuanlong Liu ◽  
Xingwang Han ◽  
Huijian Wang

The application of data mining technology in power field mainly focuses on the application of power defect text and dispatching text. However, the power operation and maintenance data contains a lot of information about power equipment suppliers. Taking the operation and maintenance text involving power equipment suppliers as an example, this paper summarizes the theme of operation and maintenance text and studies the evaluation model of power equipment suppliers. The next sentence prediction analysis model of single round dialogue text based on transformer bidirectional encoder prediction and cosine similarity weighting is proposed, which can effectively divide the topic of dialogue text. Aiming at the semantic richness and complexity of power operation and maintenance text, a supplier evaluation model based on text emotion analysis is proposed. Based on the expansion of the entries and attributes of the existing power ontology dictionary, the dialogue emotion analysis rules are established to realize the normal evaluation of power equipment suppliers.

2021 ◽  
Vol 14 (1) ◽  
pp. 12
Xu Chen ◽  
Jun Li ◽  
Wenxin Han ◽  
Shudong Liu

Tourism destination image perception aims to depict the urban tourism image from the perspective of the perception of tourists, which, therefore, sheds new light on the advancement and innovation of urban tourism. The model proposed in this study can effectively describe the image perception of a tourism destination, with its research conclusions providing a vital referential basis for the sustainable development of urban tourism. Combined with LDA, we construct the research framework of tourism destination image perception and then take the online comments of popular scenic spots in Wuhan on Ctrip Travel as an example. The results show that four aspects are included in tourists’ perception of the city image of Wuhan: experience, history culture, leisure service, and tourist destination. Among them, the social network of the experience dimension is most closely related. In addition, emotion analysis illustrates that tourists’ emotional tendencies tend to be positive under the four perceptual dimensions.

Ansh Mehta

Abstract: Previous research on emotion recognition of Twitter users centered on the use of lexicons and basic classifiers on pack of words models, despite the recent accomplishments of deep learning in many disciplines of natural language processing. The study's main question is if deep learning can help them improve their performance. Because of the scant contextual information that most posts offer, emotion analysis is still difficult. The suggested method can capture more emotion sematic than existing models by projecting emoticons and words into emoticon space, which improves the performance of emotion analysis. In a microblog setting, this aids in the detection of subjectivity, polarity, and emotion. It accomplishes this by utilizing hash tags to create three large emotion-labeled data sets that can be compared to various emotional orders. Then compare the results of a few words and character-based repetitive and convolutional neural networks to the results of a pack of words and latent semantic indexing models. Furthermore, the specifics examine the transferability of the most recent hidden state representations across distinct emotional classes and whether it is possible to construct a unified model for predicting each of them using a common representation. It's been shown that repetitive neural systems, especially character-based ones, outperform pack-of-words and latent semantic indexing models. The semantics of the token must be considered while classifying the tweet emotion. The semantics of the tokens recorded in the hash map may be simply searched. Despite these models' low exchange capacities, the recently presented training heuristic produces a unity model with execution comparable to the three solo models. Keywords: Hashtags, Sentiment Analysis, Facial Recognition, Emotions.

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