Sentiment Analysis Using Deep Learning Techniques: A Review <s> B. Recursive Neural Network (RNN) <s> Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases. <s> BIB001 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> B. Recursive Neural Network (RNN) <s> Sentiment analysis has now become a popular research problem to tackle in NLP field. However, there are very few researches conducted on sentiment analysis for Chinese. Progress is held back due to lack of large and labelled corpus and powerful models. To remedy this deficiency, we build a Chinese Sentiment Treebank over social data. It concludes 13550 labeled sentences which are from movie reviews. Furthermore, we introduce a novel Recursive Neural Deep Model (RNDM) to predict sentiment label based on recursive deep learning. We consider the problem of classifying one sentence by overall sentiment, determining a review is positive or negative. On predicting sentiment label at sentence level, our model outperforms other commonly used baselines, such as Naive Bayes, Maximum Entropy and SVM, by a large margin. <s> BIB002 </s> Sentiment Analysis Using Deep Learning Techniques: A Review <s> B. Recursive Neural Network (RNN) <s> The underground economy is a key component in cyber carding crime ecosystems because it provides a black marketplace for cyber criminals to exchange malicious tools and services that facilitate all stages of cyber carding crime. Consequently, black market sellers are of particular interest to cybersecurity researchers and practitioners. Malware/carding sellers are critical to cyber carding crime since using malwares to skim credit/debit card information and selling stolen information are two major steps of conducting such crime. In the underground economy, the malicious product/service quality is reflected by customers' feedback. In this paper, we present a deep learning-based framework for identifying top malware/carding sellers. The framework uses snowball sampling, thread classification, and deep learning-based sentiment analysis to evaluate sellers' product/service quality based on customer feedback. The framework was evaluated on a Russian carding forum and top malware/carding sellers from it were identified. Our framework contributes to underground economy research as it provides a scalable and generalizable framework for identifying key cybercrime facilitators. <s> BIB003
The recursive neural network (RNN)  lies in supervised learning. It contains a tree structure which is settled before training and the nodes can have different matrices. There is no need of reconstruction of input in RNN. The proposed work BIB002 builds a Treebank for chines sentiments of social data to overcome the deficiency of labeled and large corpus in existing models. To predict the labels at sentence level i.e positive or negative, the Recursive Neural Deep Model (RNDM) was proposed and achieved high performance than SVM, Nave Bayes and Maximum Entropy. 2270 movie reviews were collected from the website and Chinese word segmentation tool ICTCLAS was used to segment these reviews. Five classes were settled for each sentence and standford parser applied for sentence parsing. The proposed model improved the prediction of sentiment labels of sentences by concluding 13550 chines sentences and 14964 words. ME and NB performs higher with contrastive conjunction structure than baselines with great margin. In this study BIB001 , a model comprising RNTN (Recursive Neural Tensor Network) and Sentiment Treebank has been proposed to correctly clarify the compositional effects at different levels of phrases, i.e., positive and negative phrases.The proposed model was compared with all the existing models. The proposed model outperformed the previous models with the 45.5% accuracy. In existing models, the meaning of long phrases cannot be expressed effectively by semantic word spaces, so for sentiment detection, more rich and supervised evaluation and training resources are needed as it requires more influential composition models. The RNTN achieved 80.7% accuracy in sentiment prediction by performing fine-grained labeling over all the phrases and outperformed previous models. This study BIB003 has contributed a generalized and scaled framework to recognize top carding/malware sellers. The model is based on deep learning for sentiment analysis and used in thread classification and snowball sampling to assess the quality of sellers service/product by analyzing the customer feedback. The evaluation of proposed model has been conducted on Russian carding forum and a web crawler was used to gather the conversation stuff from the forum. A sentiment tree bank has been used and it was trained by using recursive neural tensor network on online review corpus. For evaluating the validity and effectiveness, two experiments were conducted in which proposed model was compared with Nave Bayes, KNN and SVM based models. This study has searched out the sellers who are highly rated for malicious services/products and the effectiveness of deep learning for recognizing these sellers. Results have been indicated that Deep learning techniques accomplish superior outcomes than the shallow classifiers and it was established that the carding sellers have fewer ratings than malware sellers. Table II , presenting the analysis of best approaches based on recursive neural network.