Opinion retrieval from blogs

Zhang, Wei; Yu, Clement; Meng, Weiyi

doi:10.1145/1321440.1321555

Cited by 112 publications

(93 citation statements)

References 20 publications

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“…A large body of work so far have dealt with different aspects of sentiment analysis, mainly sentiment extraction [1,2,7,28], classification [8,19,25], retrieval [11,12,29], summarization [3,17], and presentation [14]. The core application areas are finance [5,7,9], reviews [8,19,25], politics [16,24], and news [13].…”

Section: Related Workmentioning

confidence: 99%

A large-scale sentiment analysis for Yahoo! answers

Küçüktunç

Cambazoğlu

Weber

et al. 2012

Proceedings of the Fifth ACM International Conference on Web Search and Data Mining

120

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Sentiment 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.

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Section: Related Workmentioning

confidence: 99%

A large-scale sentiment analysis for Yahoo! answers

Küçüktunç

Cambazoğlu

Weber

et al. 2012

Proceedings of the Fifth ACM International Conference on Web Search and Data Mining

120

View full text Add to dashboard Cite

show abstract

“…Extensive work on opinion detection and retrieval has dealt with techniques to boost opinionated documents in retrieval [14,28,32,33]. Prior work focusing on opinion diversity is very recent: Demartini and Siersdorfer describe a study about opinions in search results as given by popular search engines for controversial queries [11].…”

Section: Related Workmentioning

confidence: 99%

Sentiment diversification with different biases

Aktolga

Allan

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Prior search result diversification work focuses on achieving topical variety in a ranked list, typically equally across all aspects. In this paper, we diversify with sentiments according to an explicit bias. We want to allow users to switch the result perspective to better grasp the polarity of opinionated content, such as during a literature review. For this, we first infer the prior sentiment bias inherent in a controversial topic -the 'Topic Sentiment'. Then, we utilize this information in 3 different ways to diversify results according to various sentiment biases: (1) Equal diversification to achieve a balanced and unbiased representation of all sentiments on the topic; (2) Diversification towards the Topic Sentiment, in which the actual sentiment bias in the topic is mirrored to emphasize the general perception of the topic; (3) Diversification against the Topic Sentiment, in which documents about the 'minority' or outlying sentiment(s) are boosted and those with the popular sentiment are demoted.Since sentiment classification is an essential tool for this task, we experiment by gradually degrading the accuracy of a perfect classifier down to 40%, and show which diversification approaches prove most stable in this setting. The results reveal that the proportionality-based methods and our SCSF model, considering sentiment strength and frequency in the diversified list, yield the highest gains. Further, in case the Topic Sentiment cannot be reliably estimated, we show how performance is affected by equal diversification when actually an emphasis either towards or against the Topic Sentiment is desired: in the former case, an average of 6.48% is lost across all evaluation measures, whereas in the latter case this is 16.23%, confirming that bias-specific sentiment diversification is crucial.

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“…This is mainly due to the recent growth of data available in the World Wide Web, especially of those that reflect people's opinions, experiences and feelings [12]. Early opinion mining studies focus on document level sentiment analysis concerning movie or product reviews [13,14] and posts published on web pages or blogs [15].…”

Section: Sentiment Analysis and Classification Modelsmentioning

confidence: 99%

Large Scale Implementations for Twitter Sentiment Classification

et al. 2017

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Sentiment Analysis on Twitter Data is indeed a challenging problem due to the nature, diversity and volume of the data. People tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide spectrum of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since no one can invest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample is not representative in order to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this work, we develop two systems: the first in the MapReduce and the second in the Apache Spark framework for programming with Big Data. The algorithm exploits all hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification method of diverse sentiment types in a parallel and distributed manner. Moreover, the sentiment analysis tool is based on Machine Learning methodologies alongside Natural Language Processing techniques and utilizes Apache Spark's Machine learning library, MLlib. In order to address the nature of Big Data, we introduce some pre-processing steps for achieving better results in Sentiment Analysis as well as Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Finally, the proposed system was trained and validated with real data crawled by Twitter, and, through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable while confirming the quality of our sentiment identification.

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Opinion retrieval from blogs

Cited by 112 publications

References 20 publications

A large-scale sentiment analysis for Yahoo! answers

A large-scale sentiment analysis for Yahoo! answers

Sentiment diversification with different biases

Large Scale Implementations for Twitter Sentiment Classification

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