Investigating the Effect of Emoji in Opinion Classification of Uzbek Movie Review Comments

Opinion mining on social media posts has become more and more popular. Users often express their opinion on a topic not only with words but they also use image symbols such as emoticons and emoji. In this paper, we investigate the effect of emoji-based features in opinion classification of Uzbek texts, and more specifically movie review comments from YouTube. Several classification algorithms are tested, and feature ranking is performed to evaluate the discriminative ability of the emoji-based features.Comment: 10 pages, 1 figure, 3 table

Automatic Expansion of Feature-Level Opinion Lexicons

In most tasks related to opinion mining and sentiment analysis, it is necessary to compute the semantic orientation (i.e., positive or negative evaluative implications) of certain opinion expressions. Recent works suggest that semantic orientation depends on application domains. Moreover, we think that semantic orientation depends on the specific targets (features) that an opinion is applied to. In this paper, we introduce a technique to build domainspecific, feature-level opinion lexicons in a semi-supervised manner: we first induce a lexicon starting from a small set of annotated documents; then, we expand it automatically from a larger set of unannotated documents, using a new graph-based ranking algorithm. Our method was evaluated in three different domains (headphones, hotels and cars), using a corpus of product reviews which opinions were annotated at the feature level. We conclude that our method produces feature-level opinion lexicons with better accuracy and recall that domain-independent opinion lexicons using only a few annotated documents

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Optical tomography: Image improvement using mixed projection of parallel and fan beam modes

Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam

Regression and Learning to Rank Aggregation for User Engagement Evaluation

User engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves. Ranking the items in social media websites based on the amount of user participation in them, can be used in different applications, such as recommender systems. In this paper, we consider a tweet containing a rating for a movie as an instance and focus on ranking the instances of each user based on their engagement, i.e., the total number of retweets and favorites it will gain. For this task, we define several features which can be extracted from the meta-data of each tweet. The features are partitioned into three categories: user-based, movie-based, and tweet-based. We show that in order to obtain good results, features from all categories should be considered. We exploit regression and learning to rank methods to rank the tweets and propose to aggregate the results of regression and learning to rank methods to achieve better performance. We have run our experiments on an extended version of MovieTweeting dataset provided by ACM RecSys Challenge 2014. The results show that learning to rank approach outperforms most of the regression models and the combination can improve the performance significantly.Comment: In Proceedings of the 2014 ACM Recommender Systems Challenge, RecSysChallenge '1