Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

The fast growth of Internet and social media has resulted in a significant quantity of texts based review that is posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user's review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user's. In contrast to traditional classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%

Research On Text Classification Based On Deep Neural Network

Text classification is one of the classic tasks in the field of natural language processing. The goal is to identify the category to which the text belongs. Text categorization is widely used in email detection, sentiment analysis, topic marking and other fields. However, good text representation is the key to improve the performance of natural language processing tasks such as text classification. Traditional text representation adopts bag-of-words model or vector space model, which not only loses the context information of the text, but also faces the problems of high latitude and high sparsity. In recent years, with the increase of data and the improvement of computing performance, the use of deep learning technology to represent and classify texts has attracted great attention. Convolutional neural network, recurrent neural network and recurrent neural network with attention mechanism are used to represent the text, and then to classify the text and other natural language processing tasks, all of which have better performance than the traditional methods. In this paper, we design two sentence-level text representation and classification models based on the deep network. The details are as follows: (1) Text representation and classification model based on bidirectional cyclic and convolutional neural networks-BRCNN. Brcnn's input is the word vector corresponding to each word in the sentence; After using cyclic neural network to extract word order information in sentences, convolution neural network is used to extract higher-level features of sentences. After convolution, the maximum pool operation is used to obtain sentence vectors. At last, softmax classifier is used for classification. Cyclic neural network can capture the word order information in sentences, while convolutional neural network can extract useful features. Experiments on eight text classification tasks show that BRCNN model can get better text feature representation, and the classification accuracy rate is equal to or higher than that of the prior art.. (2) A text representation and classification model based on attention mechanism and convolutional neural network-ACNN. ACNN model uses the recurrent neural network with attention mechanism to obtain the context vector; Then convolution neural network is used to extract more advanced feature information. The maximum pool operation is adopted to obtain a sentence vector; At last, the softmax classifier is used to classify the text. Experiments on eight text classification benchmark data sets show that ACNN improves the stability of model convergence, and can converge to an optimal or local optimal solution better than BRCNN

Dynamic Classification of Sentiments from Restaurant Reviews Using Novel Fuzzy-Encoded LSTM

User reviews on social media have sparked a surge in interest in the application of sentiment analysis to provide feedback to the government, public and commercial sectors. Sentiment analysis, spam identification, sarcasm detection and news classification are just few of the uses of text mining. For many firms, classifying reviews based on user feelings is a significant and collaborative effort. In recent years, machine learning models and handcrafted features have been used to study text classification, however they have failed to produce encouraging results for short text categorization. Deep neural network based Long Short-Term Memory (LSTM) and Fuzzy logic model with incremental learning is suggested in this paper. On the basis of F1-score, accuracy, precision and recall, suggested model was tested on a large dataset of hotel reviews. This study is a categorization analysis of hotel review feelings provided by hotel customers. When word embedding is paired with LSTM, findings show that the suggested model outperforms current best-practice methods, with an accuracy 81.04%, precision 77.81%, recall 80.63% and F1-score 75.44%. The efficiency of the proposed model on any sort of review categorization job is demonstrated by these encouraging findings

Narrow convolutional neural network for arabic dialects polarity classification

The complexities and tangles of Arabic dialect in orthography and morphology typically make the sentimental analysis quite challenging. Moreover, most of the classification approaches have addressed this problem based on hand-crafted features. Since the Arabic language has multi-dialects and the language has no word-based order, the extraction process and the classification tasks are more difficult and time consuming. Deep neural network approaches applied to the Arabic language colloquial are very limited. These deep learning approaches typically comprise a structure that is very complex for small quantities of data. The structures are based on wide convolutional networks that are not capable of capturing the entire semantic and sentiment features for Arabic dialects. In this paper, a narrow structure of the convolutional neural network (CNN) has been proposed in order to obtain the tweets representations and classify the Arabic tweets into five, three and two polarities. Sensitivity analysis has been conducted to evaluate the impact of various combination structural properties, such as the number of convolutional filters, pooling size, and filter size on the classification performances. The proposed Arabic narrow convolutional neural network (NCNN) has captured the entire semantic and sentiment information contained in the tweet by maximizing the features of the detector's range. The NCNN performances were estimated to be at its optimum when structured by three convolutional layers, each one followed by the max pooling layer. The model has been developed without using lexicon resources and lexical features or augmented the dataset with extra training data. The narrow model is the first baseline model for Arabic dialects sentiment classifications for a sentence level as it is the first narrow CNN model addressing the Arabic Dialect tweets. NCNN model achieved the lowest macro average mean absolute error (MAE M ) for five polarity and higher Macro average recall (P) for three and two polarities on the SemEval-2017 Arabic dialect Twitter datasets when compared to the other state-of-the-art approaches

Context-FOFE Based Deep Learning Models for Text Classification and Modeling

Text classification is a fundamental task in natural language processing. Many recently proposed deep learning models have leveraged context information in documents and achieved great successes. However, most of these models use complicated recurrent structures to handle the variable-length text and to record context information, which are hard to train. In this case, we propose a simple and efficient encoding scheme called context-FOFE that can encode context of variable-length documents into fixed-size representations. Our encoding is unique and reversible for any text sequence. Based on the encoded representations of documents, we further use two feed-forward neural network models and a generative HOPE model for text classification and modeling. We tested the models on the 20 Newsgroups text classification dataset and the IMDB sentiment analysis dataset. Experimental results show that our models can achieve competitive performance as the existing best models while using much simpler context encoding mechanism and network structure

Aspect-Based Sentiment Analysis on Mobile Game Reviews Using Deep Learning

This paper proposes an aspect-based sentiment analysis method on mobile game reviews using deep learning, which can make better use of massive mobile game reviews data to judge users\u27 emotional tendencies for different attributes of the game at a fine-grained level. Specifically, there are three models in our sentiment analysis method. The baseline model includes Bi-LSTM, FCN, and CRF for sentiment collocation extraction, matching, and classification. The iterative model updates the neural network structure and effectively improves the model\u27s recall rate in the experiments. The joint model is based on the information passing mechanism and further improves the comprehensive performance of the model. We crawled more than 100,000 game review items from two well-known Chinese game review websites Bilibili and Taptap and manually annotated 3,000 items to construct the experiment dataset. Several experiments have been carried out to evaluate our methods. The experimental results show that our methods have achieved good results

Trip Advisor Hotel Reviews: Text Classification Model

Previous customers profoundly influence the purchasing or booking decision of potential customers, and, therefore, it is imperative for businesses to assess and evaluate the social media reviews on their services or products. The aim of this project was to introduce deep learning as a tool for analyzing and evaluating customer reviews in the hotel industry. More specifically, we aimed to design several recurrent neural networks (RNN) models to analyze hotel reviews and reactions gathered from a popular travel accommodation website, TripAdvisor. To analyze the reviews, four LSTM RNN models with different labels and a convolutional neural network (CNN) model were developed. All models were evaluated to determine the most suitable model for hyperparameter tuning and distinguish the best performing model multi-class text classification. Based on the performance metrics, LSTM RNN was reported to be promising for sentiment analysis and the LSTM RNN with three classes and five classes achieved the best performance outcomes compared to the other models