Entity Level Contextual Sentiment Detection of Topic Sensitive Influential Twitterers Using SentiCircles

Sheik, Reshma; Philip, Sebin S.; Sajeev, Arjun; Sreenivasan, Sinith; Jose, Georgin

doi:10.1007/978-981-10-3223-3_19

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…In this study, we will use SentiCircle to reobtained the value of the polarities of each data with Opinion Lexicon on each Aspect to redefine the polarity value on new opinion word [16]. Process flow can be seen in Figure 4.…”

Section: Senticirclementioning

confidence: 99%

Sentiment Analysis Based on The Aspect of Culinary and Restaurant Review Using Latent Dirichlet Allocation and Support Vector Machine to Improve the Profitability of Culinary Business and Restaurant in Surabaya

Ajipangestu¹,

Sarno²

2022

Proceedings of the 3rd International Conference on Business and Management of Technology (ICONBMT 2021)

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Food is always close to us, along with the growing population, the food business will continue to grow. During the Covid-19 Pandemic, even though many people stay at home, they occasionally want to buy their favorite food outside the home, whether it's buying directly onsite or online. The aspect categorization is carried out by combining the LDA method and Semantic Similarity to categorize tweets into four culinary aspects (1) Price, (2) Taste, (3) Place, and (4) Service. The best performance of aspect categorization is by combining the LDA and TF-ICF 100% for term extension. Next, the classification stage with Word Embedding to extract features using GloVe and SVM with three-parameter modifications of the SVC method: (1) C-SVC, (2) Linear SVC, and (3) SVCnu with various kernel changes to get the best results. Then an increase in classification accuracy is carried out using SentiCircle. The results of this study show that aspects of service (4) Has a review with a high negative sentiment that reaches 10.869% compared to sentiment reviews on other aspects in percent (price: 4.348, taste: 6.522, place: 4.521) so that business owners culinary needs to make improvements to pay more attention to customer service to reduce the number of negative reviews on this service aspect. The results also show that changes in sentiment (on positive or negative sentiment) are influenced by the aspect of each review.

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Section: Senticirclementioning

confidence: 99%

Sentiment Analysis Based on The Aspect of Culinary and Restaurant Review Using Latent Dirichlet Allocation and Support Vector Machine to Improve the Profitability of Culinary Business and Restaurant in Surabaya

Ajipangestu¹,

Sarno²

2022

Proceedings of the 3rd International Conference on Business and Management of Technology (ICONBMT 2021)

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“…ere are also many other methods of extracting terms for the specific sentiment classification [20]. Sheik et al [21] proposed the sentinel method to establish the sentiment circle through the Cartesian coordinate system for the sentiment classification of sentences. Alzubi et al [22] presented a Collaborative Adversarial Network (CAN) model for paraphrase identification.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Blog Sentiment Classification Based on MD-HFCE

Yang

Tian

2021

Security and Communication Networks

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In recent years, the rapid growth of multimodal information has become an important factor affecting the results of sentiment analysis. However, a few state-of-the-art works take into account the multimodal features and sentiment fuzziness. To this end, a fuzzy method is proposed for assessing sentiment intensity in this paper. Firstly, based on the visual-text conversion network (CNN-LSTM), as well as sentiment optimization through SentiBank and SentiBridge, the visual features are normalized to the text features. At the same time, the emotional features of the extracted audio will be predicted by the random forest algorithm. Subsequently, the sentiment characteristics are processed by dual hesitant fuzzification to form positive and negative sentiment intensity factors. Finally, a classification method, that is, MD-HFCE (multilayer dual hesitant fuzzy comprehensive evaluation), fuzzy comprehensive evaluation method improved by Mamdani fuzzy reasoning, is proposed to realize the multifeature fuzzy sentiment classification based on the comprehensive sentiment dictionary. The classification results are applicable to the topics of sentiment monitoring. The experimental results show that the proposed algorithm can effectively realize feature integration and improve the average sentiment classification accuracy of multimodal blogs to 82.2%.

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The Multifaceted Concept of Context in Sentiment Analysis

Kumar

Garg

2020

Advances in Intelligent Systems and Computing

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Entity Level Contextual Sentiment Detection of Topic Sensitive Influential Twitterers Using SentiCircles

Cited by 4 publications

References 2 publications

Sentiment Analysis Based on The Aspect of Culinary and Restaurant Review Using Latent Dirichlet Allocation and Support Vector Machine to Improve the Profitability of Culinary Business and Restaurant in Surabaya

Sentiment Analysis Based on The Aspect of Culinary and Restaurant Review Using Latent Dirichlet Allocation and Support Vector Machine to Improve the Profitability of Culinary Business and Restaurant in Surabaya

Multimodal Blog Sentiment Classification Based on MD-HFCE

The Multifaceted Concept of Context in Sentiment Analysis

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