Browsing by Subject "Sentiment analysis"
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Item Open Access Aspect based opinion mining on Turkish tweets(2012) Akbaş, EsraUnderstanding opinions about entities or brands is instrumental in reputation management and decision making. With the advent of social media, more people are willing to publicly share their recommendations and opinions. As the type and amount of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. Sentiment classification aims to identify the polarity of sentiment in text. The polarity is predicted on either a binary (positive, negative) or a multi-variant scale as the strength of sentiment expressed. Text often contains a mix of positive and negative sentiments, hence it is often necessary to detect both simultaneously. While classifying text based on sentiment polarity is a major task, analyzing sentiments separately for each aspect can be more useful in many applications. In this thesis, we investigate the problem of mining opinions by extracting aspects of entities/topics on collection of short texts. We focus on Turkish tweets that contain informal short messages. Most of the available resources such as lexicons and labeled corpus in the literature of opinion mining are for the English language. Our approach would help enhance the sentiment analyses to other languages where such rich sources do not exist. After a set of preprocessing steps, we extract the aspects of the product(s) from the data and group the tweets based on the extracted aspects. In addition to our manually constructed Turkish opinion word list, an automated generation of the words with their sentiment strengths is proposed using a word selection algorithm. Then, we propose a new representation of the text according to sentiment strength of the words, which we refer to as sentiment based text representation. The feature vectors of the text are constructed according to this new representation. We adapt machine learning methods to generate classifiers based on the multi-variant scale feature vectors to detect mixture of positive and negative sentiments and to test their performance on Turkish tweets.Item Open Access A large-scale sentiment analysis for Yahoo! Answers(ACM, 2012) Küçüktunç, O.; Cambazoğlu, B. B.; Weber, I.; Ferhatosmanoğlu, HakanSentiment extraction from online web documents has recently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limited to the identification of sentiments and do not investigate the interplay between sentiments and other factors. In this work, we use a sentiment extraction tool to investigate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sentiments in the context of a large online question answering site. We start our analysis by looking at direct correlations, e.g., we observe more positive sentiments on weekends, very neutral ones in the Science & Mathematics topic, a trend for younger people to express stronger sentiments, or people in military bases to ask the most neutral questions. We then extend this basic analysis by investigating how properties of the (asker, answerer) pair affect the sentiment present in the answer. Among other things, we observe a dependence on the pairing of some inferred attributes estimated by a user's ZIP code. We also show that the best answers differ in their sentiments from other answers, e.g., in the Business & Finance topic, best answers tend to have a more neutral sentiment than other answers. Finally, we report results for the task of predicting the attitude that a question will provoke in answers. We believe that understanding factors influencing the mood of users is not only interesting from a sociological point of view, but also has applications in advertising, recommendation, and search. Copyright 2012 ACM.Item Open Access Multi-label sentiment analysis on 100 languages with dynamic weighting for label imbalance(Institute of Electrical and Electronics Engineers, 2021-07-19) Yılmaz, Selim Fırat; Kaynak, Ergün Batuhan; Koç, Aykut; Dibeklioğlu, Hamdi; Kozat, Süleyman SerdarWe investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics, and social sciences. In particular, we introduce a sentiment analysis framework in multi-label setting as it obeys Plutchik's wheel of emotions. We introduce a novel dynamic weighting method that balances the contribution from each class during training, unlike previous static weighting methods that assign non-changing weights based on their class frequency. Moreover, we adapt the focal loss that favors harder instances from single-label object recognition literature to our multi-label setting. Furthermore, we derive a method to choose optimal class-specific thresholds that maximize the macro-f1 score in linear time complexity. Through an extensive set of experiments, we show that our method obtains the state-of-the-art performance in seven of nine metrics in three different languages using a single model compared with the common baselines and the best performing methods in the SemEval competition. We publicly share our code for our model, which can perform sentiment analysis in 100 languages, to facilitate further research.Item Open Access Multi-label sentiment analysis on 100 languages with dynamic weighting for label imbalance(Institute of Electrical and Electronics Engineers Inc., 2023-01-01) Yılmaz, Selim Fırat; Kaynak, Ergün Batuhan; Koç, Aykut; Dibeklioğlu, Hamdi; Kozat, Süleyman SerdarWe investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics, and social sciences. In particular, we introduce a sentiment analysis framework in multi-label setting as it obeys Plutchik’s wheel of emotions. We introduce a novel dynamic weighting method that balances the contribution from each class during training, unlike previous static weighting methods that assign non-changing weights based on their class frequency. Moreover, we adapt the focal loss that favors harder instances from single-label object recognition literature to our multi-label setting. Furthermore, we derive a method to choose optimal class-specific thresholds that maximize the macro-f1 score in linear time complexity. Through an extensive set of experiments, we show that our method obtains the state-of-the-art performance in seven of nine metrics in three different languages using a single model compared with the common baselines and the best performing methods in the SemEval competition. We publicly share our code for our model, which can perform sentiment analysis in 100 languages, to facilitate further research.Item Open Access Text categorization using syllables and recurrent neural networks(2017-07) Yar, ErsinWe investigate multi class categorization of short texts. To this end, in the third chapter, we introduce highly efficient dimensionality reduction techniques suitable for online processing of high dimensional feature vectors generated from freely-worded text. Although text processing and classification are highly important due to many applications such as emotion recognition, advertisement selection, etc., online classification and regression algorithms over text are limited due to need for high dimensional vectors to represent natural text inputs. We overcome such limitations by showing that randomized projections and piecewise linear models can be efficiently leveraged to significantly reduce the computational cost for feature vector extraction from the tweets. We demonstrate our results over tweets collected from a real life case study where the tweets are freely-worded and unstructured. We implement several well-known machine learning algorithms as well as novel regression methods and demonstrate that we can significantly reduce the computational complexity with insignificant change in the classification and regression performance.Furthermore, in the fourth chapter, we introduce a simple and novel technique for short text classification based on LSTM neural networks. Our algorithm obtains two distributed representations for a short text to be used in classification task. We derive one representation by processing vector embeddings corresponding to words consecutively in LSTM structure and taking average of the produced outputs at each time step of the network. We also take average of distributed representations of the words in the short text to obtain the other representation. For classification, weighted combination of both representations are calculated. Moreover, for the first time in literature we propose to use syllables to exploit the sequential nature of the data in a better way. We derive distributed representations of the syllables and feed them to an LSTM network to obtain the distributed representation for the short text. Softmax layer is used to calculate categorical distribution at the end. Classification performance is evaluated in terms of AUC measure. Experiments show that utilizing two distributed representations improves classification performance by 2%. Furthermore, we demonstrate that using distributed representations of syllables in short text categorization also provides performance improvements.