Location based Twitter Opinion Mining using Common-Sense Information

Jain, Amita; Jain, Minni

doi:10.18311/gjeis/2017/15616

Cited by 12 publications

(10 citation statements)

References 2 publications

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“…Spatial correlation with sentiments, especially for positive sentiments, is also demonstrated in research performed using the United States Geo-tagged data [14]. The evidence for relating location to sentiment can be shown even on a countrylevel, with research showing that different areas in the same country have different sentiment towards the same topic [15], [16].…”

Section: A Location and Sentimentmentioning

confidence: 76%

Sentiment Analysis by Fusing Text and Location Features of Geo-Tagged Tweets

Lim

Ting

2020

IEEE Access

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Twitter sentiment analysis provides valuable feedback from public emotion concerning certain events or products. Current research has been focused on obtaining sentiment features from vectorized lexical and syntactic feature from tweets, without further context. In this paper, we demonstrated how vectorized location information could be combined with word embeddings to produce a hybrid representation, which has resulted in an improvement on a tweet sentiment classification task. The location information of the geo-tagged tweets provided further context, which was useful for a sentiment classification task. The tweets investigated contained a set of geo-tagged tweets. The word embeddings of these tweets were combined with the geo-tagged tweets' vectorized location features to form a sentiment feature set of geo-tagged tweets. The sentiment feature set was incorporated into a convolution neural network and a bi-directional long short-term memory network for the tasks of training and predicting of sentiment classification labels. This hybrid representation is compared with the baseline GloVe model through a few experiments, and the results have shown that the incorporation of vectorized location information has resulted in improvement of the accuracy for the twitter sentiment classification task.

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Section: A Location and Sentimentmentioning

confidence: 76%

Sentiment Analysis by Fusing Text and Location Features of Geo-Tagged Tweets

Lim

Ting

2020

IEEE Access

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“…Step 3: assigning polarities Since negation handling is done successfully in the previous step, hence the polarities assigned in this step are expected to be relevant. In this step, all the collected words are assigned polarities: i.e., positive, negative or neutral (Jain and Jain, 2017). The algorithm used for polarity detection is based on ConceptNet ontology (Jain and Jain, 2017).…”

Section: Sentiment Analysis Of 'Cashless Economy'mentioning

confidence: 99%

Performing opinion mining and analytical study for cashless transactions

Vij

Jain

Tayal

2019

IJFSE

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Cashless economy and digital payments are one of the hot topics of research that are gaining enormous popularity day by day. It is primarily due to their ability to combat issues of corruption, back money, illegal transactions and counterfeit money. With the increase in number of options of digital payments, people are inclining themselves towards ease of payment. This paper focuses on two aspects: firstly, it provides an analytical study performed in the field of cashless transactions and digital payments. Secondly, it finds out the opinion of the people about cashless transactions using a hybrid approach of type 2 fuzzy logic and hesitant fuzzy sets for polarity assignment. A sentiment analysis on 'cashless economy' has been carried out using data obtained from Twitter in the form of tweets.

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“…Numerous research has taken place over the past years that aimed at developing methods and tools for extracting information from Twitter communications in different fields, including several in medicine and healthcare (see [14][15][16][17][18][19][20][21][22][23][24][25][26]). Many of these research have focused on developing novel tools using advanced linguistic and sentiment analysis techniques (see for example, [15,[27][28][29][30][31][32]), while others have used qualitative hybrid (a combination of quantitative and qualitative techniques for analysis of Twitter data) (see for example, [33][34]). Again, some researchers have aimed at developing tools for capturing some specific information from Twitter such as user locations (see for example, [31,32]).…”

Section: Related Workmentioning

confidence: 99%

“…Many of these research have focused on developing novel tools using advanced linguistic and sentiment analysis techniques (see for example, [15,[27][28][29][30][31][32]), while others have used qualitative hybrid (a combination of quantitative and qualitative techniques for analysis of Twitter data) (see for example, [33][34]). Again, some researchers have aimed at developing tools for capturing some specific information from Twitter such as user locations (see for example, [31,32]).…”

Section: Related Workmentioning

confidence: 99%

A Method for Retrieval of Tweets About Hospital Patient Experience

Walters

Chowdhury

2018

Lecture Notes in Computer Science

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Analysis of Twitter communications can capture data on hospital patient experience, and this will be more appropriate for hospital management and patient care because the data represent patients' and carers' experience about something as they happen. This paper reports on the development and testing of a semi-automatic method for retrieval of subsets of Twitter communications representing hospital patient experience on different topics and subtopics. Twelve main topics of discussions on patient experience have been identified. Furthermore, it has been demonstrated that it is possible to retrieve tweets on most of the topics by using pre-defined search strings comprising various terms that represent a given topic.

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Location based Twitter Opinion Mining using Common-Sense Information

Cited by 12 publications

References 2 publications

Sentiment Analysis by Fusing Text and Location Features of Geo-Tagged Tweets

Sentiment Analysis by Fusing Text and Location Features of Geo-Tagged Tweets

Performing opinion mining and analytical study for cashless transactions

A Method for Retrieval of Tweets About Hospital Patient Experience

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