[PDF] Top 20 Facebook as a Corpus for Emoticons-Based Sentiment Analysis

... in sentiment analysis. In this scenario, emoticons can be valuable cues for deriving intended ...intended sentiment which other text based approaches will fail to ...of emoticons ... See full document

... our corpus of 1.9 million Facebook comments using the above tools to calculate scores and indicators for each ...party’s Facebook pages, aggregated results for each month and compared them to the ... See full document

... The proposed transliteration approach includes four important steps: (1) pretreatment of the Ara- bic corpus and the Arabizi message. (2) Proposal and application of the rules for the Algerian Ara- bizi. (3) ... See full document

... sites like Twitter and Facebook. Although the news genre has received much less atten- tion within the Sentiment Analysis commu- nity, news plays an important role in exhibit- ing the reality and has ... See full document

... annotated corpus was the micro-blogging platform Twitter 56%, other platforms %18 where covering Facebook, Newspapers and Penn Arabic Tree ...the corpus was reviews and comments ... See full document

... In the first version of the annotation tool (see Figure 2), the user was asked to mark text within the title or the ab- stract. Then the marked text could be assigned to one of the four PICO classes with a single button ... See full document

... “Sentiment Analysis on Twitter Data” suggested the hybrid approach that classifies the tweets from twitter dataset in sentiment categories like positive , negative, and ...are corpus ... See full document

... “A Sentiment Analysis based Approach to Facebook User Recommendation” discussed about system to offer new friends who have similar interests but having different ...technique based on a ... See full document

... Abstract: Sentiment analysis is the process of computationally identifying and categorizing opinions expressed in text to determine the users’ attitude towards a particular product is positive, negative or ... See full document

... We extracted the list of videos for each language semi- automatically: after querying the YouTube API for the names of our products and some keywords (e.g., “commer- cial”), we manually filtered the output to exclude ... See full document

... the sentiment words in the scope of negation *7+, ...the sentiment word the word “good” become “good-NOT” in Das and Chen system, but some research scholars reported that this technique has not proven best ... See full document

... For sentiment analysis, the TOTM leverages hashtags, men- tions, emoticons, and strong sentiment words that are present in ...utilises sentiment lexicons to in- corporate ... See full document

... Numerous sentiment analysis applications make usage of a sentiment ...brid sentiment lexicon acquisition. The ap- proach is corpus-based and thus suitable for languages lacking ... See full document

... In (Go et al., 2009), authors used Twitter to collect train- ing data and then to perform a sentiment search. The ap- proach is similar to (Read, 2005). The authors construct corpora by using emoticons to ... See full document

... Previous work has shown that traditional text clas- sification approaches can be quite effective when applied to the sentiment analysis problem. Models such as Na¨ıve Bayes (NB), Maximum Entropy (ME) and ... See full document

... tweet can contain. Although they are short, twitter mes- sages can be composed of a significant amount of infor- mation in a compressed form (Bifet and Frank, 2010). In addition, tweets may also convey sarcasm, mixed ... See full document

... sentimental corpus is given in existing studies, such as film reviews corpus[1], product reviews corpus [2], news reviews corpus[3], and so ...sentimental corpus is carried out in ... See full document

... Korean Sentiment Analysis Corpus named KOSAC. KOSAC is a corpus consisting of 332 news articles taken from the Sejong Syntactic Parsed ...The corpus includes 7,713 sentence subjectivity ... See full document

... Current sentiment analysis research mostly re- lies on manually annotated training data (Nakov et ...perform sentiment analysis on tweets posted in under-resourced language, such as Indone- ... See full document

... Training. The word embeddings are learned on an unsupervised corpus containing 300M German tweets. We apply a skip-gram model of window- size 5 and filter words that occur less than 15 times (Severyn and ... See full document