A framework for Twitter events detection, differentiation and its application for retail brands

Kolchyna, Olga; Souza, Tharsis T. P.; Treleaven, Philip; Aste, Tomaso

doi:10.1109/ftc.2016.7821630

Cited by 13 publications

(11 citation statements)

References 21 publications

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“…CRF needs huge number of features to work properly and the rule-based method depends on the grammatical accuracy of sentences and it can only identify explicit aspects but fails to extract implicit aspects. Besides this, sometimes incorrect aspects have been tagged by using parts-of-speech (POS) tagger [10,12] as parts-of-speech tagger considers all the noun or noun phrases as aspect terms. But all nouns are not always relevant aspect terms.…”

Section: Introductionmentioning

confidence: 99%

A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

Ray¹,

Chakrabarti

2020

ACI

104

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Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

Ray¹,

Chakrabarti

2020

ACI

104

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“…Sakaki, et al [11] calculates the probability of earthquake occurrence in Japan by fitting the exponential distribution model after sentiment analysis of Twitter data. Kolchyna et al [12] considers the difference of event space, time or topic information, and proposes a method to detect through the evolution of events. Different event clusters can be identified through event evolution features.…”

Section: Related Workmentioning

confidence: 99%

Sina Weibo Bursty Event Detection Method

Chen

Liang

2019

IEEE Access

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In response to the recent frequent outbreaks of bursty events, if the supervision is not strengthened, the grievances caused by it will have an extremely negative impact on society. Therefore, how to effectively detect bursty events in social networks has become the focus of research. In order to eliminate the interference of local daily events generated by noisy data, this paper proposes a bursty event detection method for quantifying the influence of microblog text. Through the analysis of high-impact microblogs, the burst words are mined, the potential bursty event data sets are constructed, and the k-means cluster analysis method is applied to detect the event. In order to verify the method, this paper compares with the commonly used detection methods on the real dataset. The experimental results show that the method has certain improvement in accuracy and efficiency.

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“…Our analysis is conducted on a set of five listed retail brands with stocks traded in the US equity market, which we monitor during the period from November The choice of companies is bounded by the Twitter sentiment analytics dataset provided by [11].…”

Section: Datasetmentioning

confidence: 99%

“…In [11] we proposed a new model for sentiment classification using Twitter. We combined the traditional lexicon approach with a support vector machine algorithm to achieve better predictive performance.…”

Section: Introductionmentioning

confidence: 99%

“…We combined the traditional lexicon approach with a support vector machine algorithm to achieve better predictive performance. In the present work, we use the dataset of sentiment analytics from [11] to investigate the interplay between the Twitter sentiment extracted from listed retail brands, and financial stock returns and volatility. We verify whether there is statistically-significant information in this relationship and also compare it to a corresponding analysis using sentiment from traditional newswires.…”

Section: Introductionmentioning

confidence: 99%

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Twitter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry

Souza,

Kolchyna,

Treleaven

et al. 2015

Preprint

Self Cite

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This paper presents a financial analysis over Twitter sentiment analytics extracted from listed retail brands. We investigate whether there is statistically-significant information between the Twitter sentiment and volume, and stock returns and volatility. Traditional newswires are also considered as a proxy for the market sentiment for comparative purpose. The results suggest that social media is indeed a valuable source in the analysis of the financial dynamics in the retail sector even when compared to mainstream news such as the Wall Street Journal and Dow Jones Newswires.

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A framework for Twitter events detection, differentiation and its application for retail brands

Cited by 13 publications

References 21 publications

A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

Sina Weibo Bursty Event Detection Method

Twitter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry

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