A Survey of Sentiment Analysis Based on Transfer Learning <s> I. INTRODUCTION <s> A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. <s> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> I. INTRODUCTION <s> Social networks such as Twitter are considered a rich resource of information about actual world actions of all types. Several efforts have been dedicated to trend detection on Twitter i.e., the current popular topics of conversation among its users. However, despite these efforts, sentiment analysis is not taken into account. Sentiment analysis is the field of study that analyzes people’s opinions and moods. Therefore, applying sentiment analysis to tweets related to a trending topic also enables to know if people are talking positively or negatively about it, thus providing important information for real-time decision making in various domains. On the basis of this understanding, we propose SentiTrend, a system for trend detection on twitter and its corresponding sentiment analysis. In this paper, we present the SentiTrend’s architecture and functionality. Also, the evaluation results concerning the effectiveness of our approach to trend detection and sentiment analysis are presented. Our proposal obtained encouraging results with an average F-measure of 80.7 % for sentiment classification, and an average F-measure 80.0 % and 75.5 % for trend detection. <s> BIB002 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> I. INTRODUCTION <s> Twitter is an emerging platform to express the opinion on various issues. Plenty of approaches like machine learning, information retrieval and NLP have been exercised to figure out the sentiment of the tweets. We have used movie reviews as our data set for training as well as testing and merged the naive bayes and adjective analysis for finding the polarity of the ambiguous tweets. Experimental outputs reveal that the overall accuracy of the process is improved using this model. Firstly we have applied naive bayes on collected tweets which results in set of truly polarized and falsely polarized tweets. False polarized set is further processed with adjective analysis to determine the polarity of tweets and classify it to be positive or negative. <s> BIB003 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> I. INTRODUCTION <s> With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics,have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide a research on twitter data streams.General challenges and applications of Sentiment Analysis on Twitter are also discussed in this paper. <s> BIB004 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> I. INTRODUCTION <s> Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data. <s> BIB005
Sentiment analysis is a subdivision of text mining, which uses natural language processing and related computer technology to automatically extract or classify emotions in text  . The research object includes various information on the web such as hot topics which attracted users on social media. Sentiment analysis has a very wide range of applications. For instance in the consumer industry, user preference for different commodities can be obtained through the analysis of product reviews to help companies adjust sales strategies and make decisions. In social media BIB002 - BIB003 , sentiment analysis of event comments plays an important role in public opinion control and emergency detection. Since the beginning of 2000, sentiment analysis has become one of the most active research fields in natural language processing (NLP)  . In recent years, there have been many studies on sentiment analysis  , BIB004 , BIB005 . Zhang et al.  studied the sentiment analysis based on deep learning, and discussed the recent papers from three aspects that include aspect level, sentence level and document level. The associate editor coordinating the review of this manuscript and approving it for publication was Huimin Lu. Kharde and Sonawane BIB004 gave a research review of sentiment analysis techniques based on Twitter data, compared existing analysis techniques such as machine learning and lexiconbased methods, and discussed the challenges and applications of sentiment analysis on Twitter. Cambria BIB005 divided the basic tasks of sentiment analysis into sentiment recognition and polarity detection, pointing out that the former focuses on extracting a set of emotional tags. In the current surveys of sentiment analysis, most of the sentiment analysis methods are classified from two perspectives. One is according to the granularity of text analysis, which is divided into three aspects: document level, statement level and aspect level. The other is according to the principle of method, it is mainly divided into three types: rule-based, machine learning, and deep learning. However, with the increasing volume of information, the collected data that need to be analyzed are huge, chaotic and irregular. This has brought more difficulties for sentiment analysis such as large data labeling and high cost of computing. Although the traditional sentiment analysis methods have some advantages, they are more limited due to massive data in practical application. Therefore, researchers combined sentiment analysis with transfer learning. Transfer learning BIB001 is an important research direction in machine VOLUME 7, 2019 This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ FIGURE 1. CNN model architecture for processing a sentence  . learning. It utilizes the similarity of data, data distribution or task. In this survey, we comprehensively summarize the sentiment analysis based on transfer learning, and hope to help people have a more all-sided and deep understanding of sentiment analysis. The main contributions of this survey are as follows: (1) The sentiment analysis methods are divided into traditional methods, deep learning methods, and transfer learning methods. And the point is on the summary of sentiment analysis methods based on transfer learning. (2) Summarize and compare the frequently used datasets of current sentiment analysis. (3) Discuss the challenges and opportunities of the current sentiment analysis.