Text Sentiment Classification in aspect level is one of the hottest research topics in the field of natural language processing. The purpose of the aspect-level Sentiment analysis is to determine the polarity of the text according to a particular aspect. Recently, various methods have been developed in order to determine Sentiment polarity of the text at the aspect level,however, these studies have not yet been able to model well the complementary effects of the context and aspect in the polarization detection process. Here, we present ACTSC, a method for determining the Sentiment polarity of the text based on separate embedding of aspects and context. In the first step, ACTSC deals with the separate modelling of the aspects and context in order to extract new representation vectors. Next, by combining generative representations of aspect and context, it determines the corresponding polarity to each particular aspect using a short-term memory network and a self-attention mechanism. The experimental results in the SemEval2014 dataset in both the restaurant and laptop categories show that ACTSC is able to improve the accuracy of the aspect-based Sentiment Classification compared to the latest proposed methods.

Background and Objectives: Twitter Sentiment Classification is one of the most popular fields in information retrieval and text mining. Millions of people of the world intensity use social networks like Twitter. It supports users to publish tweets to tell what they are thinking about topics. There are numerous web sites built on the Internet presenting Twitter. The user can enter a Sentiment target and seek for tweets containing positive, negative, or neutral opinions. This is remarkable for consumers to investigate the products before purchase automatically. Methods: This paper suggests a model for Sentiment Classification. The goal of this model is to investigate what is the role of n-grams and sampling techniques in Sentiment Classification application using an ensemble method on Twitter datasets. Also, it examines both binary and multiple Classifications, which are classified datasets into positive, negative, or neutral classes. Results: Twitter Classification is an outstanding problem, which has very few free resources and not available due to modified authorization status. However, all Twitter datasets are not labeled and free, except for our applied dataset. We reveal that the combination of ensemble methods, sampling techniques, and n-grams can improve the accuracy of Twitter Sentiment Classification. Conclusion: The results confirmed the superiority of the proposed model over state-of-the-art systems. The highest results obtained in terms of accuracy, precision, recall, and f-measure.

Aspect-level Sentiment Classification is an essential issue in the Sentiment analysis that intends to resolve the Sentiment polarity of a specific aspect mentioned in the input text. The recent methods have discovered the roles of some aspects in Sentiment polarity Classification and have developed various techniques to assess the Sentiment polarity of each aspect in the text. However, these studies do not pay enough attention to the need for vectors to be optimal for the aspects. In order to address this issue, in the present work, we suggest a Hierarchical Attention-based Method (HAM) for the aspect-based polarity Classification of the text. HAM works in a hierarchically manner. Firstly, it extracts an embedding vector for the aspects. Next, it employs these aspect vectors with information content to determine the Sentiment of the text. The experimental findings on the SemEval2014 dataset show that HAM can improve the accuracy by up to 6. 74% compared to the state-of-the-art methods in the aspect-based Sentiment Classification task.

Sentiment Classification of opinions is a field of Natural Language Processing which has been considered in recent years by researchers due to popularity of Internet stores and the possibility of expressing opinions about sold goods or services. To train classifier models, we need labeled datasets, but as there are not rich labeled samples and as labeling is a difficult and time-consuming process, we must employ labeled samples of other domains. In this article, a new method for binary Classification of opinions is proposed based on multi-domain transfer learning. The proposed method tries to adapt different domains by using Structural Correspondence Learning; and based on repetitive procedure of the boosting algorithm, a weight is assigned to classified samples of different domains and the class of each opinion is specified by merging these classifiers. Weighting the dataset samples to boost the process of Classification based on the Adaboost algorithm and combining it with the Structural Corresponding Learning is the most important innovation of the current research. The Amazon dataset of four different domains, each one containing 1000 positive and 1000 negative opinions is used for training the proposed model. Accuracy measures of %89. 64, %93. 97, %92. 39 and %90. 17 are obtained for Electronics, DVD, Books and Kitchen domains, respectively. It illustrates that the proposed method is very effective compared with the similar methods.

This research explores the prominent signals and presents an effective approach to identify emotional experiences and mental states based on EEG signals. First, PCA is used to reduce the data’, s dimensionality from 2K and 1K down to 10 and 15 while improving the performance. Then, regarding the insufficient high-quality training data for building EEG-based recognition methods, a multi-generator conditional GAN is presented for the generation of high-quality artificial data that covers a more complete distribution of actual data by utilizing different generators. Finally, to perform Classification, a new hybrid LSTM-SVM model is introduced. The proposed hybrid network attained overall accuracy of 99. 43% in EEG emotion state Classification and showed an outstanding performance in identifying the mental states with accuracy of 99. 27%. The introduced approach successfully combines two prominent targets of machine learning: high accuracy and small feature size, and demonstrates a great potential to be utilized in future Classification tasks.

Background and Objectives: The lack of a suitable tool for the analysis of conversational texts in Persian language has made various analyzes of these texts, including Sentiment Analysis, difficult. In this research, it has we tried to make the understanding of these texts easier for the machine by providing PSC, Persian Slang Convertor, a tool for converting conversational texts into formal ones, and by using the most up-to-date and best deep learning methods along with the PSC, the Sentiment learning of short Persian language texts for the machine in a better way.Methods: Be made More than 10 million unlabeled texts from various social networks and movie subtitles (as dialogue texts) and about 10 million news texts (as official texts) have been used for training unsupervised models and formal implementation of the tool. 60,000 texts from the comments of Instagram social network users with positive, negative, and neutral labels are considered as supervised data for training the emotion Classification model of short texts. The latest methods such as LSTM, CNN, BERT, ELMo, and deep processing techniques such as learning rate decay, regularization, and dropout have been used. LSTM has been utilized in the research, and the best accuracy has been achieved using this method.Results: Using the official tool, 57% of the words of the corpus of conversation were converted. Finally, by using the formalizer, FastText model and deep LSTM network, the accuracy of 81.91 was obtained on the test data.Conclusion: In this research, an attempt was made to pre-train models using unlabeled data, and in some cases, existing pre-trained models such as ParsBERT were used. Then, a model was implemented to classify the Sentiment of Persian short texts using labeled data.

With the explosive growth of social media such as Twitter and Instagram, reviews on e-commerce websites, and comments on news websites, individuals and organizations are increasingly using analyzing opinions in these media for their decision-making and designing strategies. Sentiment analysis is one of the techniques used to analyze users' opinions in recent years. The Persian language has specific features and thereby requires unique methods and models to be adopted for Sentiment analysis, which are different from those in English and other languages. This paper identifies the characteristics and limitations of the Persian language. Sentiment analysis in each language has specified prerequisites, hence, the direct use of methods, tools, and resources developed for the English language in Persian has its limitations. The present study aims to investigate and compare previous Sentiment analysis studies on Persian texts and describe views presented in articles published in the last decade. First, the Sentiment analysis levels, approaches, and tasks are described. Then, a detailed survey of the applied Sentiment analysis methods used for Persian texts is presented, and previous works in this field are discussed. The advantages and disadvantages of each proposed method are demonstrated. Moreover, the publicly available Sentiment analysis resources of Persian texts are studied, and the characteristics and differences of each are highlighted. As a result, according to the recent development of the Sentiment analysis field, some issues and challenges not being addressed in Persian texts are listed, and some guidelines are provided for future research on Persian texts. Future requirements of Persian text for improving the Sentiment analysis system are detailed.

Sentiment analysis in social networks has been an active research field since 2000 and it is highly useful in the decisionmaking process of various domains and applications. In Sentiment analysis, the goal is to analyze the opinion texts posted in social networks and other web-based resources to extract the necessary information from them. The data collected from various social networks and web sites do not possess a structured format, and this unstructured format is the main challenge for facing such data. It is necessary to represent the texts in the form of a text representation model to be able to analyze the content to overcome this challenge. Afterward, the required analysis can be done. The research on text modeling started a few decades ago, and so far, various models have been proposed for performing this modeling process. The main purpose of this paper is to evaluate the efficiency and effectiveness of a number of commons and famous text representation models for Sentiment analysis. This evaluation is carried out by using these models for Sentiment Classification by ensemble methods. An ensemble classifier is used for Sentiment Classification and after preprocessing, the texts is represented by selected models. The selected models for this study are TF-IDF, LSA, Word2Vec, and Doc2Vec and the used evaluation measures are Accuracy, Precision, Recall, and F-Measure. The results of the study show that in general, the Doc2Vec model provides better performance compared to other models in Sentiment analysis and at best, accuracy is 0. 72.