A Survey of Sentiment Analysis Based on Transfer Learning <s> A. TRADITIONAL SENTIMENT ANALYSIS <s> The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8p on the full test set, but the accuracy rises above 95p when the algorithm is allowed to abstain from classifying mild words. <s> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> A. TRADITIONAL SENTIMENT ANALYSIS <s> Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques. <s> BIB002 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> A. TRADITIONAL SENTIMENT ANALYSIS <s> This paper addresses the issue of sentiment word identification given an opinionated sentence, which is very important in sentiment analysis tasks. The most common way to tackle this problem is to utilize a readily available sentiment lexicon such as HowNet or SentiWordNet to determine whether a word is a sentiment word. However, in practice, words existing in the lexicon sometimes can not express sentiment tendency in a certain context while other words out of the lexicon do express. To address this challenge, this paper presents an approach based on maximum-entropy classification model to identify sentiment words given an opinionated sentence. Experimental results show that our approach outperforms baseline lexicon-based methods. <s> BIB003 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> A. TRADITIONAL SENTIMENT ANALYSIS <s> The maximum entropy (ME) method is a powerful supervised machine learning technique that is useful for various tasks. In this paper, we introduce new studies that successfully employ ME for natural language processing (NLP) problems including machine translation and information extraction. Specifically, we demonstrate, using simulation results, three applications of ME for NLP: estimation of categories, extraction of important features, and correction of error data items. We also evaluate the comparative performance of the proposed ME methods with other state-of-the-art approaches. <s> BIB004 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> A. TRADITIONAL SENTIMENT ANALYSIS <s> Considering user behavior, this paper has built a Chinese sentiment lexicon based on improved SO-PMI algorithm. Sematic lexicons were used to classify the sentiment of the collected Chinese hotel reviews. The experiment has compared the feature extraction between CHI and sentiment lexicons to find out different classification performances. The results indicate that feature extraction based on sentiment lexicon gains higher F1. The performance of classification method “Basic Semantic Lexicon + BOOL + NB” gains 92.40% of F1. Based on different sentiment lexicons, the experimental results shows that (SO-A) and (SO-P) is slightly better than NB classifier. Therefore, it would be effective to use ((SO-A) and (SO-P) as text sentiment classifiers. The experiment also finds out the method “Hotel Reviews Semantic Lexicon using improved SO-PMI algorithm +((SO-A)” gains the highest F1 which is 92.84%. The results reveal that improved SO-PMI does more effective on weight calculation and sentiment lexicon building. <s> BIB005 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> A. TRADITIONAL SENTIMENT ANALYSIS <s> SNS is one of the most effective communication tools and it has brought about drastic changes in our lives. Recently, however, a phenomenon called flaming or backlash becomes an imminent problem to private companies. A flaming incident is usually triggered by thoughtless comments/actions on SNS, and it sometimes ends up damaging to the company's reputation seriously. In this paper, in order to prevent such unexpected damage to the company's reputation, we propose a new approach to sentiment analysis using a Naive Bayes classifier, in which the features of tweets/comments are selected based on entropy-based criteria and an empirical rule to capture negative expressions. In addition, we propose a semi-supervised learning approach to relabeling noisy training data, which come from various SNS media such as Twitter, Facebook, blogs and a Japanese textboard called ‘2-channel’. In the experiments, we use four data sets of users' comments, which were posted to different SNS media of private companies. The experimental results show that the proposed Naive Bayes classifier model has good performance for different SNS media, and a semi-supervised learning effectively works for the data consisting of long comments. In addition, the proposed method is applied to detect flaming incidents, and we show that it is successfully detected. <s> BIB006 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> A. TRADITIONAL SENTIMENT ANALYSIS <s> Studying the relationship between public sentiment and stock prices has been the focus of several studies. This paper analyzes whether the sentiment expressed in Twitter feeds, which discuss selected companies and their products, can indicate their stock price changes. To address this problem, an active learning approach was developed and applied to sentiment analysis of tweet streams in the stock market domain. The paper first presents a static Twitter data analysis problem, explored in order to determine the best Twitter-specific text preprocessing setting for training the Support Vector Machine (SVM) sentiment classifier. In the static setting, the Granger causality test shows that sentiments in stock-related tweets can be used as indicators of stock price movements a few days in advance, where improved results were achieved by adapting the SVM classifier to categorize Twitter posts into three sentiment categories of positive, negative and neutral (instead of positive and negative only). These findings were adopted in the development of a new stream-based active learning approach to sentiment analysis, applicable in incremental learning from continuously changing financial tweet streams. To this end, a series of experiments was conducted to determine the best querying strategy for active learning of the SVM classifier adapted to sentiment analysis of financial tweet streams. The experiments in analyzing stock market sentiments of a particular company show that changes in positive sentiment probability can be used as indicators of the changes in stock closing prices. <s> BIB007 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> A. TRADITIONAL SENTIMENT ANALYSIS <s> Sparse coding has been widely used for feature encoding in recent years. However, the encoded parameters' similarity is ignored with sparse coding. Besides, the label information from which class the local feature is extracted is also ignored. To solve this problem, in this paper, we propose a novel feature encoding method called label constrained sparse coding (LCSC) for visual representation. The visual similarities between local features are jointly considered with the corresponding label information of local features. This is achieved by combining the label constraints with the encoding of local features. In this way, we can ensure that similar local features with the same label are encoded with similar parameters. Local features with different labels are encoded with dissimilar parameters to increase the discriminative power of encoded parameters. Besides, instead of optimizing for the coding parameter of each local feature separately, we jointly encode the local features within one sub-region in the spatial pyramid way to combine the spatial and contextual information of local features. We apply this label constrained sparse coding technique for classification tasks on several public image datasets to evaluate its effectiveness. The experimental results shows the effectiveness of the proposed method. <s> BIB008 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> A. TRADITIONAL SENTIMENT ANALYSIS <s> We propose a supervised model for expanding an opinion lexicon for Twitter.We combine automatically annotated tweets with existing hand-made opinion lexicons.We use POS tags and associations between words and sentiment as word-level features.Expanded words are mapped to a positive, negative, and neutral distribution.We outperform the performance obtained by using PMI semantic orientation alone. Opinion lexicons, which are lists of terms labeled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations when applied to social media messages such as tweets (posts in Twitter), because they are unable to capture the diversity of informal expressions commonly found in this type of media.In this article, we present a method that combines information from automatically annotated tweets and existing hand-made opinion lexicons to expand an opinion lexicon in a supervised fashion. The expanded lexicon contains part-of-speech (POS) disambiguated entries with a probability distribution for positive, negative, and neutral polarity classes, similarly to SentiWordNet.To obtain this distribution using machine learning, we propose word-level attributes based on (a) the morphological information conveyed by POS tags and (b) associations between words and the sentiment expressed in the tweets that contain them. We consider tweets with both hard and soft sentiment labels. The sentiment associations are modeled in two different ways: using point-wise-mutual-information semantic orientation (PMI-SO), and using stochastic gradient descent semantic orientation (SGD-SO), which learns a linear relationship between words and sentiment. The training dataset is labeled by a seed lexicon formed by combining multiple hand-annotated lexicons.Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by using PMI-SO alone. This is significant because PMI-SO is a state-of-the-art measure for establishing world-level sentiment. Additionally, we show that lexicons created with our method achieve significant improvements over SentiWordNet for classifying tweets into polarity classes, and also outperform SentiStrength in the majority of the experiments. <s> BIB009
Traditional sentiment analysis methods include lexiconbased approaches and non-neural network classifier approaches. The lexicon-based method BIB001 - BIB005 refers to judging the sentiment polarity by constructing an emotional dictionary, extracting emotional values, etc. Turney and Littman BIB001 proposed the use of semantic polarity (ISA) algorithm to analyze text sentiment tendency. Hu and Liu BIB002 generated a dictionary containing positive and negative sentiment words through seed words in WordNet, and classify sentences according to the dictionary. Bravo-Marquez et al. BIB009 introduced a method of expanding the opinion lexicon in a supervised manner. An improved SO-PMI algorithm has been proposed by Yang et al. BIB005 , which is more effective for emotional computing and emotional vocabulary modeling. The lexicon-based approach is easy to operate, but it relies heavily on the sentiment dictionary, and ignores the connection of the positional between words. The extraction of the text is simplistic and not comprehensive. The non-neural network classifier belongs to the supervised machine learning method, which is realized by manual labeling and training model. The most common classifiers are Naive Bayes (NB) BIB006 -  , Maximum Entropy BIB003 - BIB004 , and SVM BIB007 -  . These classifiers perform well in different classification fields such as image classification BIB008 and text classification. With the increase of data volume and the need of diverse tasks, more scholars use the neural network model based on deep learning to apply to sentiment analysis tasks and achieve better results.