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The paper presents the sentiment analysis based algorithm for evaluation of public's attitude to innovations. Domain-depended corpus for training algorithm was created. More than 200 000 articles from Russian mass media devoted to innovations were analyzed. Results of analysis represented as set of histogram. Conclusions and future work are given.
In this paper, we present an application designed to analyze news articles from Romanian mass media and extract opinions about political entities relevant to the major political stage. The application was created with the desire to study media polarization around important political events, such as legislative or presidential elections. The application uses different crawlers to extract the data from...
Polarity detection is a research topic of major interest, with many applications including detecting the polarity of product reviews. However, in some cases, the polarity of the product reviews might not be available while the polarity of the product itself might be, prohibiting the use of any form of fully supervised learning technique. This scenario, while different, is close to that of multiple...
The recent social media use of the masses in the development of information technology and the popularization of smartphones continues to rapidly increase. In particular, to understand pending issue of society, there is a tendency for local anonymous entities to increase the use of social media to gather information. In this study, the pending issue of society was examined by analyzing the non-structured...
As social media services (e.g. Wikipedia, Facebook, Twitter, Linkedin, and so on) become more and more popular, it is of greater research interest to raise the efficiency of using Sentiment Analysis to predict future opinion trends. Based on the naïve Bayes classifier, this research proposes a novel emotion classifier, CCLM (Combined CKIP Language Model), to enhance the precision of opinion classification...
Sentiment analysis emerged as an important computational domain to gain insights from snippets of texts, as social media recently gained popularity. Text mining has long been a fundamental data analytic for sentiment analysis. One of the popular preprocessing approaches in text mining is transforming text strings to word vectors which form a high-dimensional sparse matrix. This sparse matrix poses...
This paper covers design, implementation and evaluation of a system that may be used to predict future stock prices basing on analysis of data from social media services. The authors took advantage of large datasets available from Twitter micro blogging platform and widely available stock market records. Data was collected during three months and processed for further analysis. Machine learning was...
The widespread use of social media and the internet are emerging trends that offer an additional interaction channel for companies to better understand customer sentiments about their brands and products. Sentiment analysis uses text data from social media such as customer comments and reviews, which has the nature of high dimensionality. Without selection, typically there are at least thousands of...
Advent of social media has created an unprecedented environment for people to share their thoughts with the world. These online platforms like facebook, Twitter are usually the first resort people turn to in times of crisis to voice their opinions and relay other crucial information. But when it comes to detecting sentiments out of this gigantic pool of opinions, it becomes an arduous task and doing...
Arabic Twitter Sentiment Analysis has been gaining a lot of attention lately with supervised approaches being exploited widely. However, to date, there has not been an experimental study that examines how different configurations of the Bag of Words model, text representation scheme, can affect various supervised machine learning methods. The goal of the presented work is to do exactly that. Specifically,...
The world behaves in a manner showing similarity in responses to various actions, this similarity in behavior needs to be tapped. This phenomenon is called Collective Behavior. Collective behavior is the like or similar response of the members of a society to a given stimulus or suggestion. The study of collective behavior can also be applied for the college campus environment. The system developed...
Social media data consists of feedback, critiques and other comments that are posted online by internet users. Collectively, these comments may reflect sentiments that are sometimes not captured in traditional data collection methods such as administering a survey questionnaire. Thus, social media data offers a rich source of information, which can be adequately analyzed and understood. In this paper,...
With social media services becoming more and more popular, there now exists a constant stream of opinions publicly available on the Internet. In crisis situations, analysis of social media data can improve situation awareness and help authorities to provide better assistance to the affected population. The large amount of activity on social media services makes manual analysis infeasible. Thus, an...
Inferring the sentiment of social media content, for instance blog posts and forum threads, is both of great interest to security analysts and technically challenging to accomplish. This paper presents a new method for estimating social media sentiment which addresses the challenges associated with Web-based analysis. The approach formulates the task as one of learning-based text classification, models...
Social media has demonstrated itself to be a proven source of information towards the marketing of products. This unique source of data provides a rapid means of customer feedback that is used to support a number of business areas. Towards this purpose, we describe a methodology for the identification of topics associated with customer sentiment. This process first employs a Fisher Classification...
Recently, a huge wave of social media has generated significant impact in people's perceptions about technological domains. They are captured in several blogs/forums, where the themes relate to products of several companies. One of the companies can be interested to track them as resources for customer perceptions and detect user sentiments. The keyword-based approaches for identifying such themes...
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