A Survey of Sentiment Lexicons

. A Survey of Sentiment Lexicons Sagar Ahire Published 2015 Abstract This is a survey paper that introduces sentiment lexicons and explains the state of the art in the field of sentiment lexicons. Different kinds of lexicons are covered, varying in aspects such as coverage, methods of creation, lexical unit and granularity. It aims at giving a representative sampling of the field of sentiment lexicons. . . https://pdfs.semanticscholar.org/2522/de6022acf2bc7d5c12a9467d4c41f6358920.pdf... Read More

Sentiment Analysis: Concept, Analysis and Applications

. "); text-decoration-line: none;" target="_blank">Sentiment analysis is contextual mining of text which identifies and extracts subjective information in source material, and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. However, analysis of social media streams is usually restricted to just basic sentiment analysis and count based metrics. This is akin to just scratching the surface and missing out on those high value insights that are waiting to be discovered. So what should a brand do to capture that low hanging fruit? With the recent advances in deep learning, the... Read More

Gavagai Sentiment Analysis and Opinion Mining

. Sentiment Analysis and Opinion Mining Sentiment analysis, also known as opinion mining, is a practice of gauging the sentiment expressed in a text, such as a post in social media or a review on Google. Analysts typically code a solution (for example using Python), or use a pre-built analytics solution such as Gavagai Explorer. What is Sentiment Analysis? Sentiment analysis or opinion mining is a notoriously difficult sub-field of Natural Language Processing and Data Science. At the most fundamental level, the task is to take a piece of text and automatically score it for the opinions and... Read More

A Study on Sentiment Computing and Classification of Sina Weibo with Word2vec

. In recent years, Weibo has greatly enriched people's life. More and more people are actively sharing information with others and expressing their opinions and feelings on Weibo. Analyzing emotion hidden in this information can benefit online marketing, branding, customer relationship management and monitoring public opinions. Sentiment analysis is to identify the emotional tendencies of the microblog messages, that is to classify users' emotions into positive, negative and neutral. This paper presents a novel model to build a Sentiment Dictionary using Word2vec tool based on our Semantic Orientation Pointwise Similarity Distance (SO-SD) model. Then we use the Emotional Dictionary... Read More

A Joint Model for Chinese Microblog Sentiment Analysis

. Topic-based sentiment analysis for Chinese microblog aims to identify the user attitude on specified topics. In this paper, we propose a joint model by incorporating Support Vector Machines (SVM) and deep neural network to improve the performance of sentiment analysis. Firstly, a SVM Classifier is constructed using N-gram, NPOS and sentiment lexicons features. Meanwhile, a convolutional neural network is applied to learn paragraph representation features as the input of another SVM classifier. The classification results outputted by these two classifiers are merged as the final classification results. The evaluations on the SIGHAN-8 Topic-based Chinese microblog sentiment analysis task... Read More

Towards Building a High-Quality Microblog-Specific Chinese Sentiment Lexicon

. Due to the huge popularity of microblogging services, microblogs have become important sources of customer opinions. Sentiment analysis systems can provide useful knowledge to decision support systems and decision makers by aggregating and summarizing the opinions in massive microblogs automatically. The most important component of sentiment analysis systems is sentiment lexicon. However, the performance of traditional sentiment lexicons on microblog sentiment analysis is far from satisfactory, especially for Chinese. In this paper, we propose a data-driven approach to build a high-quality microblog-specific sentiment lexicon for Chinese microblog sentiment analysis system. The core of our method is a unified... Read More

Sentiment Analysis for Chinese Microblog based on Deep Neural Networks with Convolutional Extension Features

. Related research for sentiment analysis on Chinese microblog is aiming at the analysis procedure of posts. The length of short microblog text limits feature extraction of microblog. Tweeting is the process of communication with friends, so that microblog comments are important reference information for related post. A contents extension framework is proposed in this paper combining posts and related comments into a microblog conversation for features extraction. A novel convolutional auto encoder is adopted which can extract contextual information from microblog conversation as features for the post. A customized DNN(Deep Neural Network) model, which is stacked with several... Read More

Context-Aware Chinese Microblog Sentiment Classification with Bidirectional LSTM

. Recently, with the fast development of the microblog, analyzing the sentiment orientations of the tweets has become a hot research topic for both academic and industrial communities. Most of the existing methods treat each microblog as an independent training instance. However, the sentiments embedded in tweets are usually ambiguous and context-aware. Even a non-sentiment word might convey a clear emotional tendency in the microblog conversations. In this paper, we regard the microblog conversation as sequence, and leverage bidirectional Long Short-Term Memory (BLSTM) models to incorporate preceding tweets for context-aware sentiment classification. Our proposed method could not only alleviate... Read More

Chinese Microblog Sentiment Analysis Based on Sentiment Features

. As the microblog has increasingly become an information platform for netizens to share their ideas, the study on the sentiment analysis of microblog has got scholars’ wide attention both at home and abroad. The primary goal of this research is to improve the accuracy of microblog sentiment polarity classification. With a view to the characteristics of microblog, a new method of semantically related feature extraction is proposed. Firstly, the Chinese word features are selected by text presentation in VSM and computing the weight by TF*IDF. Secondly, the proposed eight microblog semantic features are extracted, including sentence sentiment judgment... Read More

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

. We introduce a method to train Quantized Neural Networks (QNNs) --- neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At train-time the quantized weights and activations are used for computing the parameter gradients. During the forward pass, QNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations. As a result, power consumption is expected to be drastically reduced. We trained QNNs over the MNIST, CIFAR-10, SVHN and ImageNet datasets. The resulting QNNs achieve prediction accuracy comparable to their 32-bit counterparts. For example, our quantized version of AlexNet with... Read More

Sentiment Target Extraction Based on CRFs with Multi-features for Chinese Microblog

. Sentiment target extraction on Chinese microblog has attracted increasing research attention. Most previous work relies on syntax, such as automatic parse trees, which are subject to noise for informal text such as microblog. In this paper, we propose a modified CRFs model for Chinese microblog sentiment target extraction. This model see the sentiment target extraction as a sequence-labeling problem, incorporating the contextual information, syntactic rules and opinion lexicon into the model with multi-features. The major contribution of this method is that it can be applied to the texts in which the targets are not mentioned in the sequence.... Read More

An approach to sentiment analysis of short Chinese texts based on SVMs

. ... Experimental results have shown that the Naive Bayes classifier performs the best. Approach to analyze the sentiment of short Chinese texts is presented in [9]. By using word2vec tool, sentiment dictionaries from NTU and HowNet are extended. ... . https://www.researchgate.net/publication/308868603_An_approach_to_sentiment_analysis_of_short_Chinese_texts_based_on_SVMs . ... Read More

A Dynamic Conditional Random Field Based Framework for Sentence-Level Sentiment Analysis of Chinese Microblog

. . https://www.researchgate.net/publication/319051638_A_Dynamic_Conditional_Random_Field_Based_Framework_for_Sentence-Level_Sentiment_Analysis_of_Chinese_Microblog .... Read More

Speech Recognition With Deep Recurrent Neural Networks

. Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates $backslash$emphdeep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep... Read More

Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary

. Micro-blog texts contain complex and abundant sentiments which reflect user's standpoints or opinions on a given topic. However, the existing classification method of sentiments cannot facilitate micro-blog topic monitoring. To solve this problem, this paper presents a sentiment analysis method for Chinese micro-blog text based on the sentiment dictionary to support network regulators' work better. First, the sentiment dictionary can be extended by extraction and construction of degree adverb dictionary, network word dictionary, negative word dictionary and other related dictionaries. Second, the sentiment value of a micro-blog text can be obtained through the calculation of the weight. Finally,... Read More

Alternating Multi-bit Quantization for Recurrent Neural Networks

. Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. For applications on the server with large scale concurrent requests, the latency during inference can also be very critical for costly computing resources. In this work, we address these problems by quantizing the network, both weights and activations, into multiple binary codes {-1,+1}. We formulate the quantization as an optimization problem. Under the key observation that once the quantization coefficients are fixed the binary codes can be derived efficiently by binary search tree,... Read More

Research on sentiment analysis of microblogging based on LSA and TF-IDF

. ... The anonymity of Weibo makes peo- ple being willing to express their real sentiments. Many existing techniques of sentiment analysis are based on sen- timent lexicons and traditional feature engineering [1]- [8]. Most of these methods need resort to external resource or manually preprocess features of words. ... . https://www.researchgate.net/publication/324462809_Research_on_sentiment_analysis_of_microblogging_based_on_LSA_and_TF-IDF .... Read More

Multi-label Chinese Microblog Emotion Classification via Convolutional Neural Network

. Recently, analyzing people’s sentiments in microblogs has attracted more and more attentions from both academic and industrial communities. The traditional methods usually treat the sentiment analysis as a kind of single-label supervised learning problem that classifies the microblog according to sentiment orientation or single-labeled emotion. However, in fact multiple fine-grained emotions may be coexisting in just one tweet or even one sentence of the microblog. In this paper, we regard the emotion detection in microblogs as a multi-label classification problem. We leverage the skip-gram language model to learn distributed word representations as input features, and utilize a Convolutional... Read More

Sentiment Analysis of Chinese Microblog Based on Stacked Bidirectional LSTM

. Sentiment analysis on Chinese microblogs has received extensive attention recently. Most previous studies focus on identifying sentiment orientation by encoding as many wordproperties as possible while they fail to consider contextual features (e.g., the long-range dependencies of words), which are however essentially important in the sentiment analysis. In this paper, we propose a Chinese sentiment analysis method by incorporating Word2Vec model and Stacked Bidirectional long short-term memory (Stacked Bi-LSTM) model. We first employ Word2Vec model to capture semantic features of words and transfer words into high dimensional word vectors. We evaluate the performance of two typical Word2Vec models:... Read More

Deep-based Ingredient Recognition for Cooking Recipe Retrieval

. Retrieving recipes corresponding to given dish pictures facilitates the estimation of nutrition facts, which is crucial to various health relevant applications. The current approaches mostly focus on recognition of food category based on global dish appearance without explicit analysis of ingredient composition. Such approaches are incapable for retrieval of recipes with unknown food categories, a problem referred to as zero-shot retrieval. On the other hand, content-based retrieval without knowledge of food categories is also difficult to attain satisfactory performance due to large visual variations in food appearance and ingredient composition. As the number of ingredients is far less... Read More

Machine learning in the cloud: How it can help you right now

. What is machine learning? Machine learning is really about the study of algorithms that have the ability to learn through patterns and, based on that, make predictions against patterns of data. It’s a better alternative to leveraging static program instructions and instead making data-driven predictions or decisions that will improve over time without human intervention and additional programming. Machine learning could be a game-changer for the business. One of the concerns, as machine learning becomes more affordable through the use of cloud platforms, is that the technology will be misapplied. This already seems to be a pattern, as... Read More