نتایج جستجو برای: sentiment analysis
تعداد نتایج: 2828323 فیلتر نتایج به سال:
Twitter has emerged as a major social media platform and generated great interest from sentiment analysis researchers. Despite this attention, state-of-the-art Twitter sentiment analysis approaches perform relatively poorly with reported classification accuracies often below 70%, adversely impacting applications of the derived sentiment information. In this research, we investigate the unique c...
Sentiment analysis is one of the most active research areas in natural language processing, web/social network mining, and text/multimedia data mining. The growing importance of sentiment analysis coincides with the popularity of social network platforms, such as Facebook, Twitter, and Flickr, which provide a rich repository of people’s opinion and sentiment about a vast spectrum of topics. Mor...
Sentiment analysis has undergone a shift from document-level analysis, where labels expresses the sentiment of a whole document or whole sentence, to subsentential approaches, which assess the contribution of individual phrases, in particular including the composition of sentiment terms and phrases such as negators and intensifiers. Starting from a small sentiment treebank modeled after the Sta...
Sentiment analysis has been shown to be a useful tool for quantitative analysis in the world of finance. Researchers have shown that the sentiment picked up from the news media can be correlated with movement of the stock market. Here we use the Harvard General Inquirer to determine the sentiment present in Reuter’s articles. After first generating positive and negative sentiment data we use th...
Sentiment analysis of online social media has attracted significant interest recently. Many studies have been performed, but most existing methods focus on either only textual content or only visual content. In this paper, we utilize deep learning models in a convolutional neural network (CNN) to analyze the sentiment in Chinese microblogs from both textual and visual content. We first train a ...
In this paper we describe our current work on Senti–TUT, a novel Italian corpus for sentiment analysis. This resource includes annotations concerning both sentiment and morpho-syntax, in order to make available several possibilities of further exploitation related to sentiment analysis. For what concerns the annotation at sentiment level, we focus on irony and we selected therefore texts on pol...
This paper describes our systems submitted to the Sentence-level and Text-level AspectBased Sentiment Analysis (ABSA) task (i.e., Task 5) in SemEval-2016. The task involves two phases, namely, Aspect Detection phase and Sentiment Polarity Classification phase. We participated in the second phase of both subtasks in laptop and restaurant domains, which focuses on the sentiment analysis based on ...
Text mining can be applied to many fields. One of the application is using text mining in digital newspaper to do politic sentiment analysis. In this paper sentiment analysis is applied to get information from digital news articles about its positive or negative sentiment regarding particular politician. This paper suggests a simple model to analyze digital newspaper sentiment polarity using na...
Lingmotif is a lexicon-based, linguistically-motivated, user-friendly, GUI-enabled, multi-platform, Sentiment Analysis desktop application. Lingmotif can perform SA on any type of input texts, regardless of their length and topic. The analysis is based on the identification of sentiment-laden words and phrases contained in the application’s rich core lexicons, and employs context rules to accou...
The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates precise in-text sentiment tagging as well a...
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