Text sentiment classification for SNS-based marketing using domain sentiment dictionary

2012 ◽  
Author(s):  
Sang-Hyun Cho ◽  
Hang-Bong Kang
Keyword(s):  
Sentiment Classification ◽  
Sentiment Dictionary

scholarly journals Multimodal Blog Sentiment Classification Based on MD-HFCE

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Baozhen Yang ◽  
Xuedong Tian
Keyword(s):  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Sentiment Classification ◽  
Comprehensive Evaluation Method ◽  
Multimodal Features ◽  
Sentiment Dictionary ◽  
Visual Text ◽  
Text Features ◽  
Negative Sentiment

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%.

scholarly journals Detection of Sentiment Analysis in Social Media using Deep Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 636-643
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Activation Function ◽  
Sentiment Classification ◽  
Network Layers ◽  
Sentiment Dictionary ◽  
Lstm Network ◽  
The Military

With the rapid climb of web page from social media, such studies as online opinion mining or sentiment analysis of text have started receiving attention from government, industry, and academic sectors. In recent years, sentiment analysis has not only emerged under knowledge fusion within the big data era, but has also become a well-liked research topic within the area of AI and machine learning. This study used the Military life PTT board of Taiwan’s largest online forum because the source of its experimental data. the aim of this study was to construct a sentiment analysis framework and processes for social media so as to propose a self-developed military sentiment dictionary for improving sentiment classification and analyze the performance of various deep learning models with various parameter calibration combinations. The experimental results show that the accuracy and F1-measure of the model that mixes existing sentiment dictionaries and therefore the self-developed military sentiment dictionary are better than the results from using existing sentiment dictionaries only. Furthermore, the prediction model trained using the activation function, Tanh, and when the amount of Bi-LSTM network layers is 2, the accuracy and F1-measure have a good better performance for sentiment classification.

Student sentiment classification model based on GRU neural network and TF-IDF algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hailong Yu ◽  
Yannan Ji ◽  
Qinglin Li
Keyword(s):  
Neural Network ◽  
Sentiment Classification ◽  
Classification Model ◽  
Research Model ◽  
Student Group ◽  
Comprehensive Performance ◽  
Sentiment Dictionary ◽  
Neural Network Algorithm ◽  
Sentence Matching

Due to the diversity of text expressions, the text sentiment classification algorithm based on semantic understanding is difficult to establish a perfect sentiment dictionary and sentence matching template, which leads to strong limitations of the algorithm. In particular, it has certain difficulties in the classification of student sentiments. Based on this, this paper analyzes the student sentiment classification model by neural network algorithm and uses the student group as an example to explore the application of neural network model in sentiment classification. Moreover, the regularization method is added to the loss function of LSTM so that the output at any time is related to the output at the previous time. In addition, the sentimental drift distribution of sentimental words on each sentimental label is added to the regularizer, and the sentimental information is merged with the two-way LSTM to allow the model to choose forward or reverse. Finally, in order to verify the research model, the performance of the model proposed in this paper is studied through experimental research. The research shows that the model proposed in this paper has better comprehensive performance than the traditional model and can meet the actual needs of students’ sentiment classification.

scholarly journals Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 307 ◽  
Author(s):  
Nina Rizun ◽  
Yurii Taranenko ◽  
Wojciech Waloszek
Keyword(s):  
Probabilistic Models ◽  
Complex Structure ◽  
Sentiment Classification ◽  
Probabilistic Methods ◽  
Semantic Relations ◽  
Semantic Structure ◽  
Scientific Contribution ◽  
Context Sensitive ◽  
Accuracy Level ◽  
Sentiment Dictionary

The research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR). The objective of this methodology is to find ways of eliminating the limitations of the discriminant and probabilistic methods for LSR revealing and customizing the sentiment classification process (SCP) to the more accurate recognition of text tonality. This objective was achieved by providing the possibility of the joint usage of the following methods: (1) retrieval and recognition of the hierarchical semantic structure of the text and (2) development of the hierarchical contextually-oriented sentiment dictionary in order to perform the context-sensitive SCP. The main scientific contribution of this research is the set of the following approaches: at the phase of LSR revealing (1) combination of the discriminant and probabilistic models while applying the rules of adjustments to obtain the final joint result; at all SCP phases (2) considering document as a complex structure of topically completed textual components (paragraphs) and (3) taking into account the features of persuasive documents’ type. The experimental results have demonstrated the enhancement of the SCP accuracy, namely significant increase of average values of recall and precision indicators and guarantee of sufficient accuracy level.

A mixed approach of statistical weighting method and unsupervised method to improve Uyghur sentiment classification

2020 ◽  
pp. 1-23
Author(s):  
Erpan Yalkun ◽  
Wushour Slamu ◽  
Raxida Turhuntay
Keyword(s):  
Machine Learning ◽  
Sentiment Classification ◽  
Weighting Method ◽  
Training Corpus ◽  
Learning Classifier ◽  
Sentence Patterns ◽  
Sentiment Dictionary ◽  
Mixed Approach ◽  
Resource Shortage ◽  
Degree Adverbs

Considering the scarcity of Uyghur sentiment resources, in this paper proposed a new combined unsupervised sentiment classification method for Uyghur text without any labeled corpora. In the first part, a Uyghur sentiment dictionary, UYSentiDict, was adopted to classify the sentences. For the sentiment vocabulary matching, both the matching of the original word and the stem were considered, and the influence of sentence patterns, negation words, and degree adverbs were further considered as well. Based on different thresholds, the sentences with higher sentiment values were selected from the lexicon-based classification results as a pseudo-labeled dataset. In the second part, different sentiment characteristics were learned from the pseudo-labeled dataset by the machine learning classifier, and the remaining categorical data were further classified. It can be concluded that the method proposed in this paper has good classification efficiency in Uyghur sentiment corpora in four different fields, and some results were performed better than the classification results of machine learning classifier. Moreover, this method is not restricted by the field of data and does not need to be marked in advance with good training corpus, and can solve the resource shortage problem in the field of Uyghur sentiment classification effectively.

scholarly journals Sentiment Classification Algorithm Based on the Cascade of BERT Model and Adaptive Sentiment Dictionary

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ruixue Duan ◽  
Zhuofan Huang ◽  
Yangsen Zhang ◽  
Xiulei Liu ◽  
Yue Dang
Keyword(s):  
Deep Learning ◽  
Mobile Network ◽  
Sentiment Classification ◽  
Language Models ◽  
Classification Model ◽  
Data Sets ◽  
Probability Ratio ◽  
Sentiment Dictionary ◽  
Accuracy Increase ◽  
Rule Method

The mobile social network contains a large amount of information in a form of commentary. Effective analysis of the sentiment in the comments would help improve the recommendations in the mobile network. With the development of well-performing pretrained language models, the performance of sentiment classification task based on deep learning has seen new breakthroughs in the past decade. However, deep learning models suffer from poor interpretability, making it difficult to integrate sentiment knowledge into the model. This paper proposes a sentiment classification model based on the cascade of the BERT model and the adaptive sentiment dictionary. First, the pretrained BERT model is used to fine-tune with the training corpus, and the probability of sentiment classification in different categories is obtained through the softmax layer. Next, to allow a more effective comparison between the probabilities for the two classes, a nonlinearity is introduced in a form of positive-negative probability ratio, using the rule method based on sentiment dictionary to deal with the probability ratio below the threshold. This method of cascading the pretrained model and the semantic rules of the sentiment dictionary allows to utilize the advantages of both models. Different sized Chnsenticorp data sets are used to train the proposed model. Experimental results show that the Dict-BERT model is better than the BERT-only model, especially when the training set is relatively small. The improvement is obvious with the accuracy increase of 0.8%.

Sign in / Sign up

Export Citation Format

Share Document