[PDF] Top 20 Sentiment Analysis on Naija Tweets

... Examining sentiments in social media poses a challenge to natural language processing because of the intricacy and variability in the dialect articulation, noisy terms in form of slang, abbreviation, acronym, emoticon, ... See full document

... necessary tweets in order to perform sentiment ...the tweets, we use naïve Bayes or SVM classifier. Performing sentiment analysis using text is a difficult ...the sentiment FP. ... See full document

... measured sentiment values with real life events. Thus, if the sentiment analysis correlates these events we have an empirical validation of the proposed ... See full document

... The Naïve Bayes Classifier is a supervised learning model which makes use of statistical method for classification. Since it’s a probabilistic model, it allows to capture the uncertainties about the model by calculating ... See full document

... the tweets where determinations are to an extraordinary degree unstructured, heterogeneous and are either positive or negative, or impartial now and ...of Sentiment Analysis on ...level ... See full document

... the analysis of the impact for businesses of their publications in social networks ...This analysis is very important because the audience manifest its opinion mostly in texts that must be processed one by ... See full document

... for sentiment Analysis in social ...Egyptian tweets and comments by using two data ...500 tweets. 310 tweets were classified as negative, 155 as positive, and 35 as ... See full document

... Twitter Sentiment Analysis is relatively ...300,000 tweets (balanced between positive, negative and neutral classes) by querying happy and sad emoticons, and newswire ...and sentiment label of ... See full document

... only sentiment analysis of the tweets, some used online tools for that as Senta[1], and some used machine learning approaches as SVM ...those tweets are legitimate or has been removed by ... See full document

... internet, tweets form twitter, customer comments from different social networking data rapidly increasing makes the summarization task challenging for ...the analysis of different hash-tags, categorization ... See full document

... world. Sentiment analysis is a process of automatically identifying the opinion of users whether it is positive, negative or neutral about an entity in a user-generated ...on sentiment ... See full document

... negative tweets) that will be used for expanding the training data in the next ...seed tweets to the other unlabeled tweets when they share similar word features, which means that the ... See full document

... Arabic tweets, one of the best results for SA to date is reported in Mourad and Darwish (2013) with ...to tweets col- lected at a later stage, due to changing topics and vo- ... See full document

... main sentiment classification experiments under the assumption that they constituted a special case which would be better handled by a different clas- ...sarcastic tweets – only a quarter of them are cor- ... See full document

... Figure 1 represents the main process. Here, historical data is taken and the data is processed, i.e., the data is tokenized and they are converted to a particular case. The document operator contains the tweets ... See full document

... All data selected for annotation comes from TWITA. The first dataset consists of 1,000 ran- domly selected tweets. The second dataset is topic- oriented, i.e. we randomly extracted 1,000 tweets from all ... See full document

... Sentiment analysis is a hot topic that’s been on research for decades, which intends to find the nature of text and classifies into positive, negative and ...retrieval, analysis and classification of ... See full document

... of sentiment analysis, organizations can take effective measures in order to maintain and improve their place in the market by assessing which products or services require improvement, from which price ... See full document

... Sentimental Analysis is the process of accessing tweets for a particular topic and predicts the sentiment of these tweets as positive or negative with the help of different machine learning ... See full document

... of sentiment analysis, that emoji embeddings learned from short descriptions outperforms a skip-gram model trained on a large collection of tweets, while avoiding the need for contexts in which emoji ... See full document