Top PDF Sentiment Analysis of Tourism Micro blog Comments

Sentiment Analysis of Tourism Micro blog Comments

Sentiment Analysis of Tourism Micro blog Comments

With the development of the living level and the internet environment, more and more people start touring and writing their comment text on the micro-blog website pages. Interactions between online users are also more frequent. The tourism micro-blog comments contain opinions with different emotional tendency and personal semantic information. Thus, mining these comments has vital benefit for both consumers and businesses. It helps the consumers to choose the right services and products and also helps the businesses to improve their services.
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TMRS: WEB Blog Application for Total Movie Review System Using Sentiment Analysis

TMRS: WEB Blog Application for Total Movie Review System Using Sentiment Analysis

The proposed system includes a determining component known as Sentiment analyzer. It is used for scoring the sentiment regarding a product. The comments from blogs are mining. There are three important tasks in sentiment analysis: determining subjectivity, determining sentiment orientation, and determining the strength of the sentiment orientation. In the proposed method, we use an unsupervised approach. An OPEN_NLP is used to find types of words and calculate subjectivity and polarity of given statement. A keyword database is used, where there are specific words related to a movie. There is also a keyword algorithm that performs searching of keywords on the text. If the keyword that is searched is found into the database, the algorithm determines whether the keyword is adjective or an adverb and then calculates the score in bipolar orientation.
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Implementation of Sentiment Comments made by Indian users

Implementation of Sentiment Comments made by Indian users

ABSTRACT: Due to the absolute amount of opinion loaded web assets such as debate meeting, analysis site, blogs and reports corpora existing in digital structure greatly of the existing study is focus on the region of sentiment analysis. People are planned to extend a structure that can classify and organize opinion or sentiment as represent in an electronic content. In this we tested on movie reviews, product reviews and Facebook comments. The consequences illustrate that a fusion arrangement can recover the organization value. In this paper, we calculate the pros and cons of the existing technique and improve the existing techniques with POS tagging and generate the score of the comments or words and predict the sentiment of the comment and then the conclusion is devised.
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Statistical data mining for Sina Weibo, a Chinese micro blog: sentiment modelling and randomness reduction for topic modelling

Statistical data mining for Sina Weibo, a Chinese micro blog: sentiment modelling and randomness reduction for topic modelling

Initial quantitative and time series analyses provide a brief description of the general patterns for the posts, but they are not sufficient for understanding what people generally posted about these companies. Thus, further textual analysis, as a complement of quantitative methods, would provide insight on the contents that people posted about. This textual analysis can be also referred to as text mining, a process of deriving the patterns and trends from texts through means such as statistical pattern learning. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, sentiment analysis, document summarisation, and entity relation modelling. It is a part of statistical pattern learning, which aims to use artificial intelligence to learn from data. In this and next two sections, we will first introduce Chinese word segmentation and term frequency analysis as basic text mining, and then explore cluster analysis for grouping posts based on their contents. Next chapters will further discuss sentiment modelling and topic modelling. For quantitative data analyses that only research the amount of posts, there are no other steps required for data pre-processing. Nevertheless, when attempting text mining, some additional steps are requisite. Unlike English, which contains spaces between adjacent words, all Chinese characters are written together. Therefore, word segmentation is essential for Chinese text as an extra step before text mining. As the segmentation influences further textual analysis, it is crucial to find a way to segment Chinese sentence accurately and efficiently.
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Targeted Sentiment to Understand Student Comments

Targeted Sentiment to Understand Student Comments

Sentiment analysis is the computational study of people’s opinions or emotions; it is a challenging prob- lem that is increasingly being used for decision making by individuals and organizations (Pang and Lee, 2008). There is a significant body of research on sentiment analysis, addressing entire documents (Agar- wal and Bhattacharyya, 2005), including blogs (Godbole et al., 2007; Annett and Kondrak, 2008) and reviews (Yi et al., 2003; Cabral and Hortacsu, 2010); sentences (Yu and Hatzivassiloglou, 2003; Nigam and Hurst, 2004) or otherwise short spans of texts such as tweets (Pak and Paroubek, 2010; Kouloumpis et al., 2011); and phrases (Wilson et al., 2005; Turney, 2002). More recent work has also addressed the task of aspect sentiment (Pontiki et al., 2015; Thet et al., 2010; Lakkaraju et al., 2014), which aims to address the sentiment toward attributes of the target entity, such as the service in a restaurant (Sauper and Barzilay, 2013), or the camera of a mobile phone (Chamlertwat et al., 2012).
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Twitter Sentiment Analysis

Twitter Sentiment Analysis

Microblogging sites, in today’s world have become a sea of data for analysts to prey on. This is because most of the individuals today are connected to some kind of microblogging site where they pull out all the hype they feel regarding anything. It won’t be wrong to say that in some way these Microblogging sites have given a right to speech to every individual who can access them. People from diverse parts of the world freely discuss , comment , post their opinions about any topic of their choosing in real time .These blogs are mostly a complain expressing a negative vibe Or an appreciation expressing a positive vibe toward any topic of their choosing . The topics people post about could be a product from an organization such as a laptop or a phone. Or it could be a famous entity Or any other thing. Most of the leading organizations in today’s era have employed analysts who have a job to derive emotions of people behind these posts. This helps them to get a proper review About their product or company which helps them know public demand and the alterations they Need to make in order to make better product in future. Therefore from the discussion above it could be concluded that these micro-blogging sites could become an asset to different organizations public or private if analysis of sentiment could be implemented on them. Sentiment analysis also known as analysis of feelings is an useful tool for analyzing different sites where people post their opinions regarding a topic of interest .With the help of this kind of analysis organizations can obtain the sentiments of the people which they post as tweets or as comments or even as review regarding a particular entity or product of interest to them .This goes in accordance with[10] who says , almost 87% people having a connection with internet check reviews before purchase. This technique could be
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Dropout prediction in MOOCs : using sentiment analysis of users' comments to predict engagement

Dropout prediction in MOOCs : using sentiment analysis of users' comments to predict engagement

The main area of implementation of sentiment analysis is mining people’s blogs, twits and posts to get information about general attitude towards various subjects from a particular product to a political party or candidate. Data obtained through sentiment analysis are believed to provide information from the source that was previously unavailable directly – public opinion and feeling. Such information could show success or failure of any particular company, service, product, party or policy as mining opinions helps companies to get their clients’ feedback very quickly and act accordingly. The same principle can be applied to the educational context. Assessing and taking into account learners' opinions and attitudes towards the course can be a very useful feedback for course developers, moreover, the feedback received just on the spot which increases its validity compared with the surveys conducted before or after the course. However, as the technique of sentiment analysis is still being developed, the reliability standards reflect this process of mastering. Up to date the relevance of 70 per cent between a computerized and human assessment is considered to be good (Kennedy, 2012).
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SPAM COMMENT DETECTION IN BLOG COMMENTS FROM BLOG RSS FEED BY MODIFIED TF-IDF ALGORITHM

SPAM COMMENT DETECTION IN BLOG COMMENTS FROM BLOG RSS FEED BY MODIFIED TF-IDF ALGORITHM

The approach described in this paper exploits Senti-WordNet [2] as lexical resource for Spam mining. The authors introduce a lexicon based method of analyzing the Spam even without any training data in this direction. Khurshid et al [3] have developed a method for identifying the words that may surprise a native speaker by comparing the distribution of all the words in a collection of randomly sampled financial texts with that of the same words in a reference collection of texts. More prolific keywords in financial texts, the chances are that such a word will be less prolific in general language texts. Once it identifies keywords, based on statistical criteria in our training collection of texts, then the system looks at the neighborhood of these keywords; and, then looks at the neighbourhood of the two word pairs and so on.This neighbourhood, established on strict statistical criteria, yields information bearing sentences in the financial domain and, it turns out sentences that typically carry sentimental information. These patterns are then used to build a finite state automaton. This automaton is then tested on an unseen set of texts – and the results vis-à-vis sentiment analysis is quite good. The authours in [4] have analyzed the various techniques for sentiment analysis in online data. Further they present a simplistic algorithm for the same which contains following.
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Sentiment Based Comments Rating Approach

Sentiment Based Comments Rating Approach

AnandKumar et el made a study that at the early times majority of the research was carried out in the field of sentiment analysis of the textual data that was available in the web. One of the difficult tasks is the classification of the sarcastic sentences. It was because of the various representations of the textual form sentences. This affected many Natural Language Processing applications. Sarcasm is one of the representations to convey the various sentiments presented .They have tried to identify the various supervised classification techniques which is mainly used for sarcasm detection and their features .They have also analyzed the results of the various techniques used on the textual data that is available in various languages on the review related sites, social media sites and also in the micro blogging sites. Their work presents the analysis of data generation and feature selection process used. They also have carried out preliminary experiment to detect sarcastic sentences in “Hindi” language. They have trained SVM classifier with 10X validation with simple Bag-Of Words as features and TF-IDF as frequency measure of the feature. They found that this is model based on the “bag-of-words” feature which accurately classified 50% of sarcastic sentences. As a result preliminary experiment which they have conducted has revealed the fact that the Bag-of-Words are not sufficient for sarcasm detection. It can be performed at three levels: document-level, sentence-level, and aspect level. However sentences can be considered as a short document which removes fundamental difference between document and sentence level.
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Research on Chinese Micro blog Sentiment Classification Based on Recurrent Neural Network

Research on Chinese Micro blog Sentiment Classification Based on Recurrent Neural Network

Micro-blog, as the burgeoning social network platform in recent years, because of its easy operation, fast spread and high flexibility, has been widely respected and used by users. With the sharp increase of text data published by users, how to quickly and effectively find interesting topics from the micro-blog text containing large information and understand the trend of public opinion has become the focus of attention at present. It is clearly inefficient and unrealistic to use traditional artificial statistical method to deal with such large and growing text data. In response to this problem, the sentiment analysis of micro-blog text comes into being. Sentiment analysis can generally be summarized as the research task of three progressive layers: sentiment information extraction, sentiment classification and sentiment retrieval [1]. The sentiment classification, that is, the viewpoint classification, is the extremely critical intermediate link in the sentiment analysis process, mainly through digging and analyzing the sentiment tendency of the information users convey. The sentiment classification of micro-blog text is aimed at micro-blog text information. The analysis of micro-blog text by sentiment classification technology can distinguish the sentiment tendencies of blog posters, so as to help enterprises or government in specific fields understand users’ needs, control public opinion, improve efficiency, better serve users and society.
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Mining Important Comments of Micro Blog Based on Feature Weighting

Mining Important Comments of Micro Blog Based on Feature Weighting

Many research efforts have been conducted on mining comments of Microblog recently. Much of the work is mainly focused on sentiment analysis in microblogs [2-5]. However, comments are important supplementary information of microblogs. More and more researchers devote their attention to it. L. Zhang, et al. [6] develop a framework based on a heuristic–systematic model for comparing the effects of content and context of microblogging posts. As the result, the purpose of retweet is to disseminate information, whereas the comment emphasizes social interaction and conversation. A. Kothari, et al. [7] apply machine learning based classification approaches for identifying comments on specific news articles from twitter. Such comments are provided with news articles to improve reader experience.
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Improved PageRank Algorithm Combined User Behavior with Topic Similarity

Improved PageRank Algorithm Combined User Behavior with Topic Similarity

In figure 2, the horizontal coordinates, in the same query conditions, the query topK% micro-blog users. The vertical coordinates, user influence coverage ratio. From figure 3, we know that the BSPR algorithm is superior than BWPR algorithm(User behavior through algorithm, retweets, comments, and other factors mentioned in the analysis of user, to calculate the influence order of users), WPR algorithm (The theme correlation algorithm, by constructing a link analysis of micro-blog users / web content based on user influence ranking) and TURank algorithm (Combined with time factor algorithm, the time factor is introduced to observe the influence of users in a certain period of time). It shows that the micro-blog data information filtering and user related behavior, the theme of similarity, the combination of the time factor and as the basis for the weight distribution is more close to the reality, and achieve a good effect.
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Agenda setting and micro-blog use: An analysis of the relationship  between Sina Weibo and newspaper agendas in China

Agenda setting and micro-blog use: An analysis of the relationship between Sina Weibo and newspaper agendas in China

And lastly, both newspapers archive electronically online; so their content is easy to access. Sample messages were retrieved from seven Sina Weibo accounts from July 23 to August 1 of 2011. During the immediate aftermath of the collision, a featured special page was con- structed by Sina as an open platform to share updated information. Five accounts were pinpointed from that feature page due to their crucial status as information sources. Two accounts belong to indi- vidual users, one account is associated with a news organization, and another two accounts have government affiliations. Between those two individual users, one sent out the very first report of this acci- dent through her Weibo account, the other posted the first piece of information requesting help from the public after the accident. Both users have gained fame for their Weibo exchanges related to this train accident. Two additional accounts – one initiated by a journalist and the other founded by the prominent newspaper for which the jour- nalist worked -- were recommended by Sina via its special coverage of the July 23rd railway collision, based on this reporter’s manuscript page and the newspaper coverage itself, both of which were known for their in-depth news coverage. In total, some 598 micro-blog exchanges and 201 news reports were collected as the sample for this study.
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Beyond Sentiment: Social Psychological Analysis of Political Facebook Comments in Hungary

Beyond Sentiment: Social Psychological Analysis of Political Facebook Comments in Hungary

2.2 Adapting NLP Tools to Social Media All of the NLP tools that were used for preproc- essing the comments were developed for a lin- guistic domain (using standard language texts, mostly newswire) that is different from the lan- guage used in Facebook comments. The latter has a high tendency for phenomena like typos and spelling errors, non-standard punctuation use, use of slang expressions, emoticons and other creative uses of characters, substitution of Hungarian accented characters by their unac- cented variants etc. For this reason, our readily available tools suffered from degradation in per- formance. To overcome this problem, we em- ployed a two-fold approach: we applied normali- zations to the input and also extended our tools to adapt them to the SM language domain.
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Rules Design in Word Segmentation of Chinese Micro Blog

Rules Design in Word Segmentation of Chinese Micro Blog

This paper proposed a Hidden Markov Model (HMM) based tokenizer for Chi- nese micro-blog texts. Comparing with normal Chinese texts, micro-blog texts contain more uncertainties. These uncer- tainties are generally aroused by the irreg- ular use of bloggers (such as network words, dialect words, wrong written char- acters, mixture of foreign words and sym- bols, etc.). Besides the lack of the annotat- ed training corpus is also a restriction in solving this task. Hence the segmentation for micro-blogs is much more difficult than that of general text, we present an HMM based segmentation model integrat- ed with a pre and post correction module. The evaluation results show that the pro- posed approach can achieve an F-measure of 90.98% on test set of 5,000 sentences.
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Recommendation System in E-Commerce using Sentiment Analysis

Recommendation System in E-Commerce using Sentiment Analysis

sentence-level sentiment analysis is the main task on opinion mining. The approach can find the specific details of the comments and has a high confidential degree, but the operation is very complex. For example, consider a laptop, its feature can be classified into performance, price, appearance, endurance time, brand and so on. Each feature or attribute is considered for which the user can specify the comment and a comprehensive evaluation in order to avoid the overgeneralization is done. Feature- specific opinion mining attracts much attention. An object is an entity. It can be a product, person, event, organization, topic or something else. It is associated with a hierarchy or taxonomy of components or a set of attributes. Meanwhile, each component also can have its own set of subcomponents or attributes. A feature is defined to show both components and attributes and it is the subject of a review. In fact, people obey the grammatical rules to organize sentences while writing articles. But while writing reviews informally, people usually neglect it and there is more chance to have grammatical mistakes as well as usage of sarcastic words in reviews. This phenomenon is especially prominent when people give away their comments after online shopping. Sentiment analysis also known as opinion mining refers to the use of natural language processing and computational linguistics to extract subjective information from the given data and classify opinions. It is a broader concept and many tasks are involved in it. The most important are as follows:
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A correlation analysis between sentimental comment and numerical 
		response in students feedback

A correlation analysis between sentimental comment and numerical response in students feedback

The results from the above graphs in Figures-2 to 4 illustrate the correlation between Overall sentiment scores and the numerical response scores of teacher evaluation aspects; e.g., Helpfulness ratings, Clarity ratings and Overall ratings. The graphs present that the moving curves between Overall sentiment scores and the numerical response scores of teacher evaluation aspects and Overall ratings are increasing in the same direction. Moreover, the statistical analysis results from Table-3 indicated a strong correlation between each pair of two variables above and support these visual correlation results. However, this work still has the limitation of the sentiment analysis, in this experimental the “positivity” and “negativity” of sentiment words based on Opinion Lexicon the lexicon resource of product reviews. Furthermore, there are some limitations of the identifying sentiment words process. The first issue is the limit to detect the nagation and return incorrect polarity; e.g., Not clear, Not helpful. The second issue is the limit to check the opinion target correctly. The specific domain lexicon resource for teacher evaluation, the improvement of nagation detection and opinion target checked for students’ comment are still needed.
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Data Set for Stance and Sentiment Analysis from User Comments on Croatian News

Data Set for Stance and Sentiment Analysis from User Comments on Croatian News

Nowadays it is becoming more important than ever to find new ways of extracting useful information from the evergrowing amount of user-generated data available online. In this paper, we describe the creation of a data set that contains news articles and corresponding comments from Croatian news outlet 24 sata. Our annotation scheme is specifically tailored for the task of detecting stances and sentiment from user comments as well as assessing if commentator claims are verifiable. Through this data, we hope to get a better understand- ing of the publics viewpoint on various events. In addition, we also explore the potential of ap- plying supervised machine learning models to automate annotation of more data.
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“Haters gonna hate”: challenges for sentiment analysis of Facebook comments in Brazilian Portuguese

“Haters gonna hate”: challenges for sentiment analysis of Facebook comments in Brazilian Portuguese

The analysis of the corpus showed that the same words spoken by different people may have polar opposite semantic orientations. We also noticed that the writers of the comments use nouns and noun phrases not only to name some entity, but al- so to build discourse objects in a way that the label they give to the discourse objects reveals an evalu- ation. We propound reflections about such prob- lems within the Discourse Analysis framework, mainly Pêcheux (1975) and Mondada and Dubois (1995).

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Spatial Analysis of Tourism Micro-entrepreneurship and Poverty in North Carolina and its Neighboring States.

Spatial Analysis of Tourism Micro-entrepreneurship and Poverty in North Carolina and its Neighboring States.

The U.S. Census Bureau surveys – County Business Patterns has data available on two types of micro businesses that are very tourism-related, micro businesses that provide arts, entertainment, and recreational facilities and micro businesses that provide accommodation and food services. Therefore, tourism micro-entrepreneurship which is the main focus of this study refers to either of these two types of micro tourism businesses. When investigating the relationships between potential predictor variables with tourism micro-entrepreneurship, two dependent variables used representing level of two types of tourism micro-entrepreneurship are actually the number of the two above-mentioned micro businesses per 100,000 people. The independent variables used include national parks, state parks, percentage of water area, historic places nationally registered, museums, unemployment rate, more formal tourism sector, self-employment rate, population density, urban-rural continuum code, high school graduation rate for people 25years old and over, per capita income, percentage of African Americans, percentage of Hispanics and percentage of vacant housing. Table 1 presents the definition of variables and data sources used in modeling the relationships between potential predictor variables and tourism micro-entrepreneurship.
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