4,749 research outputs found

    Sentiment Analysis of Afaan Oromoo Facebook Media Using Deep Learning Approach

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    The rapid development and popularity of social media and social networks provide people with unprecedented opportunities to express and share their thoughts, views, opinions and feelings about almost anything through their personal webpages and blogs or using social network sites like Facebook, Twitter, and Blogger.  This study focuses on sentiment analysis of social media content because automatically identifying and classifying opinions from social media posts can provide significant economic values and social benefits. The major problem with sentiment analysis of social media posts is that it is extremely vast, fragmented, unorganized and unstructured. Nevertheless, many organizations and individuals are highly interested to know what other peoples are thinking or feeling about their services and products. Therefore, sentiment analysis has increasingly become a major area of research interest in the field of Natural Language Processing and Text Mining. In general, sentiment analysis is the process of automatically identifying and categorizing opinions in order to determine whether the writer's attitude towards a particular entity is positive or negative. To the best of the researcher’s knowledge, there is no Deep learning approach done for Afaan Oromoo Sentiment analysis to identify the opinion of the people on social media content. Therefore, in this study, we focused on investigating Convolutional Neural Network and Long Short Term Memory deep learning approaches for the development of sentiment analysis of Afaan Oromoo social media content such as Facebook posts comments. To this end, a total of 1452 comments collected from the official site of the Facebook page of Oromo Democratic Party/ODP for the study. After collecting the data, manual annotation is undertaken. Preprocessing, normalization, tokenization, stop word removal of the sentence are performed. We used the Keras deep learning python library to implement both deep learning algorithms. Long Short Term Memory and Convolutional Neural Network, we used word embedding as a feature. We conducted our experiment on the selected classifiers. For classifiers, we used 80% training and 20% testing rule. According to the experiment, the result shows that Convolutional Neural Network achieves the accuracy of 89%. The Long Short Memory achieves accuracy of 87.6%. Even though the result is promising there are still challenges. Keywords: Sentiment Analysis; Opinionated Afaan Oromoo facebook comments; Oromo Democratic Party Facebook page DOI: 10.7176/NMMC/90-02 Publication date:May 31st 202

    Unsupervised and knowledge-poor approaches to sentiment analysis

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    Sentiment analysis focuses upon automatic classiffication of a document's sentiment (and more generally extraction of opinion from text). Ways of expressing sentiment have been shown to be dependent on what a document is about (domain-dependency). This complicates supervised methods for sentiment analysis which rely on extensive use of training data or linguistic resources that are usually either domain-specific or generic. Both kinds of resources prevent classiffiers from performing well across a range of domains, as this requires appropriate in-domain (domain-specific) data. This thesis presents a novel unsupervised, knowledge-poor approach to sentiment analysis aimed at creating a domain-independent and multilingual sentiment analysis system. The approach extracts domain-specific resources from documents that are to be processed, and uses them for sentiment analysis. This approach does not require any training corpora, large sets of rules or generic sentiment lexicons, which makes it domain- and languageindependent but at the same time able to utilise domain- and language-specific information. The thesis describes and tests the approach, which is applied to diffeerent data, including customer reviews of various types of products, reviews of films and books, and news items; and to four languages: Chinese, English, Russian and Japanese. The approach is applied not only to binary sentiment classiffication, but also to three-way sentiment classiffication (positive, negative and neutral), subjectivity classifiation of documents and sentences, and to the extraction of opinion holders and opinion targets. Experimental results suggest that the approach is often a viable alternative to supervised systems, especially when applied to large document collections

    Data analytics 2016: proceedings of the fifth international conference on data analytics

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    Social-media monitoring for cold-start recommendations

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    Generating personalized movie recommendations to users is a problem that most commonly relies on user-movie ratings. These ratings are generally used either to understand the user preferences or to recommend movies that users with similar rating patterns have rated highly. However, movie recommenders are often subject to the Cold-Start problem: new movies have not been rated by anyone, so, they will not be recommended to anyone; likewise, the preferences of new users who have not rated any movie cannot be learned. In parallel, Social-Media platforms, such as Twitter, collect great amounts of user feedback on movies, as these are very popular nowadays. This thesis proposes to explore feedback shared on Twitter to predict the popularity of new movies and show how it can be used to tackle the Cold-Start problem. It also proposes, at a finer grain, to explore the reputation of directors and actors on IMDb to tackle the Cold-Start problem. To assess these aspects, a Reputation-enhanced Recommendation Algorithm is implemented and evaluated on a crawled IMDb dataset with previous user ratings of old movies,together with Twitter data crawled from January 2014 to March 2014, to recommend 60 movies affected by the Cold-Start problem. Twitter revealed to be a strong reputation predictor, and the Reputation-enhanced Recommendation Algorithm improved over several baseline methods. Additionally, the algorithm also proved to be useful when recommending movies in an extreme Cold-Start scenario, where both new movies and users are affected by the Cold-Start problem

    Sentiment Classification using Machine Learning: A Survey

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    The World Wide Web has brought a new way of expressing the reactions of people about any product, things, and topics, etc. The sentiment Analysis of written textual content on the web is one of the text mining areas used to find out sentiments in a given text. The process of sentiment analysis is a task of detecting, extracting and classifying critiques and sentiments expressed in texts. Twitter is also a medium with the huge amount of information wherein users can view the opinion of other users that labeled into different sentiment classes such as positive, negative, and neutral and are increasingly more developing as a key element in decision making. ?Till now, there are few extraordinary problems predominating in this research community, namely, sentiment classification, feature-based classification and dealing with negations. This paper presents a survey covering the strategies and techniques of sentiment classification and demanding situations appear within the area.
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