Sentiment Analysis Using Deep Learning Techniques: A Review <s> G. Other Neural Networks <s> Sentiment analysis of short texts such as single sentences and Twitter messages is challenging because of the limited contextual information that they normally contain. Effectively solving this task requires strategies that combine the small text content with prior knowledge and use more than just bag-of-words. In this work we propose a new deep convolutional neural network that exploits from characterto sentence-level information to perform sentiment analysis of short texts. We apply our approach for two corpora of two different domains: the Stanford Sentiment Treebank (SSTb), which contains sentences from movie reviews; and the Stanford Twitter Sentiment corpus (STS), which contains Twitter messages. For the SSTb corpus, our approach achieves state-of-the-art results for single sentence sentiment prediction in both binary positive/negative classification, with 85.7% accuracy, and fine-grained classification, with 48.3% accuracy. For the STS corpus, our approach achieves a sentiment prediction accuracy of 86.4%. <s> BIB001 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> G. Other Neural Networks <s> In order to determine the sentiment polarity of Hinglish text written in Roman script, we experimented with different combinations of feature selection methods and a host of classifiers using term frequency-inverse document frequency feature representation. We carried out in total 840 experiments in order to determine the best classifiers for sentiment expressed in the news and Facebook comments written in Hinglish. We concluded that a triumvirate of term frequency-inverse document frequency-based feature representation, gain ratio based feature selection, and Radial Basis Function Neural Network as the best combination to classify sentiment expressed in the Hinglish text. <s> BIB002
In this study  to overcome the complexity in wordlevel models the character-level model have been proposed. The motivation of proposed model CDBLSTM is an existing model that is DBLSTM neural networks  . The focus of this work is only on textual content and on the polarity analysis of tweets in which a tweet is classified into two classes, i.e., positive and negative. There can be more options than positive and negative such as natural and finer but here the model is restricted only to positive and negative classes to compare with existing published results. The tweets are encoded from character level and trained by the use of CCE (categorical cross-entropy). Experiments were conducted on two datasets, first one is latest benchmark dataset for SemEval 2016 and the second one is provided by GO dataset. Adam algorithm was implemented to train all the models and the learning rate settled to 0.1. Final predictions were obtained by the model of logistic regression. Results demonstrate that proposed approach is competent for the polarity problems. By applying different experiments results shows that CDBLSTM performs better than DBLSTM and by comparing the results with Deep Convolutional Neural Network (DCNN) BIB001 which performs well on twitter SPC (sentiment polarity classification). The 85.86% accuracy was achieved on STS (Stanford Twitter Sentiment) corpus and 84.82% on SemEval-2016. In this study  , author has proposed TF-IDF, GR, and RBFNN for sentiment classification on Hinglish text. Many studies have worked on sentiment analysis of various languages such as English, Turkish, Flemmish, Spanish, Arabic and Chinese but no work has been done on Indic language. To fill this gap, the sentiments were classified in Hinglish language as it contains Hindi words along with the English. This contribution BIB001 overcomes the problem that occurs in effectively analyzing the emotions of customers toward companies in blog sphere. A neural network (NN) based technique is proposed which subordinate the advantages of Semantic orientation index and machine learning methods for the classification of sentiments effectively and quickly. The input of neural network is semantic orientation indexes. While considering the fault tolerance ability the BPN (backpropagation neural network) is selected as basic learner of proposed method. Data was collected from the blogs of real world such as from "LiveJournal" and "Review Center". Segmentation of words, SO indexes calculations, neural network trainings and performance evaluations have been conducted. Training and test sets were settled of datasets. The overall accuracy (OA), performance evaluation matrices and F1 were used. Results concluded that proposed method has enhanced the performance of classification and saved training time as compared to traditional ML and IR. This contribution BIB002 proposed a data driven supervised approach for the purpose of feature reduction and development of lexicon specific to twitter sentiment analysis about brand. Statistical analysis and n-gram were used for twitter specific www.ijacsa.thesai.org lexicon and feature reduction accordingly. The existing models SVM and nave Bayes were used for twitter specific lexicon to compete with the existing studies. The classification was done in proposed model by using artificial neural networks for twitter specific lexicon and this difference outperformed the existing models. The input matrix was parse matrix. Datasets were divided into training and test datasets to achieve better accuracy. The feature engineering phase was done through preprocessing activities for transforming the documents in simple form and to produce the vector presentation. The data was collected from Twitter API v1.0. The proposed model was applied on Justin Bieber Twitter corpus and it was established that the emotions contain high explanatory power as compared to existing studies. The proposed model has reduced the features of Justin Bieber corpus and enhanced the classification accuracy and high amount of coverage. Only six expressions were found related to Justin Bieber brand out of 181 and others were found twitter-specific. The proposed model has facilitated the Justin Bieber brand to identify the issues and views about the brand.