Learning Sentiments from Tweets with Personal Health Information

Bobicev, Victoria; Sokolova, Marina; Jafer, Yasser; Schramm, David

doi:10.1007/978-3-642-30353-1_4

Cited by 23 publications

(18 citation statements)

References 13 publications

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“…Health-related affects and emotions are often analyzed on Twitter data [6,5] and public forums dedicated to health [1,11,15]. Further, we limit the discussion to research areas directly relevant to the current project.…”

Section: Related Workmentioning

confidence: 99%

“…Agreement among seven manual annotators was computed for five classification categories, including positive, negative, mixed opinions and non-opinionated and non-relevant categories. [5] asked multiple annotators to categorize tweets into positive and negative sentiments and neutral tweets. Manual annotation was applied in multiclass sentiment classification in [18].…”

Section: Related Workmentioning

confidence: 99%

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Sentiment and Factual Transitions in Online Medical Forums

Bobicev

Sokolova

Oakes

2015

Advances in Artificial Intelligence

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Abstract. This work studies sentiment transitions on an online medical forum where users correspond in English. We work with discussions dedicated to reproductive technologies, an emotionally-charged issue. We analyze authors' activity on the forum, communication flow in discussions and sentiment flow in consecutive messages. We show how sentiment transitions can be successfully used to disambiguate sentiments. In several learning problems, we demonstrate that multi-class sentiment classification significantly improves when messages are represented by combination of affective terms and sentiment transitions (paired t-test, P=0.0011).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sentiment and Factual Transitions in Online Medical Forums

Bobicev

Sokolova

Oakes

2015

Advances in Artificial Intelligence

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“…Sentiment analysis often connects its subjects with specific online media (e.g., sentiments on consumer goods are studied on Amazon.com). Health-related emotions are studied on Twitter (Chew and Eysenbach, 2010;Bobicev et al, 2012) and online public forums (Malik and Coulson, 2010;Goeuriot et al, 2012). Reliable annotation is essential for a thorough analysis of text.…”

Section: Related Workmentioning

confidence: 99%

“…Sokolova and Bobicev (2013) evaluated annotation agreement achieved on messages gathered from a medical forum. Bobicev et al (2012) used multiple annotators to categorize tweets into positive, negative and neutral tweets. Merits of reader-centric and author-centric annotation models were discussed in (Balahur, Steinberger, 2009).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)

Lin¹,

Ku²,

Cambria³

et al. 2014

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IntroductionWelcome to the COLING 2014 Second Workshop on Natural Language Processing for Social Media (SocialNLP). SocialNLP is a new inter-disciplinary area of natural language processing (NLP) and social computing. We consider three plausible directions of SocialNLP: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using information from social networks or social media; and (3) handling new problems related to both social computing and natural language processing.Through this workshop, we anticipate to provide a platform for research outcome presentation and head-to-head discussion in the area of SocialNLP, with the hope to combine the insight and experience of prominent researchers from both NLP and social computing domains to contribute to the area of SocialNLP jointly. Also, selected and expanded versions of papers presented at SocialNLP will be published in two follow-on Special Issues of Springer Cognitive Computation (CogComp) and the International Journal of Computational Linguistics and Chinese Language Processing (IJCLCLP).The submissions to this year's workshop were again of high quality and we had a competitive selection process. We received 18 submissions, and due to a rigorous review process, we only accepted 6 of them. Thus the acceptance rate was 33%. We also have 2 invited papers. The workshop papers cover a broad range of SocialNLP-related topics, such as aspect extraction, multi-lingual sentiment analysis, sentiment feature selection, online rating prediction, sentiment sequence recognition, automatic identification, verbal behavior and persuasiveness analysis, and user classification. We had a total of 18 reviewers. We warmly thank our PC members for the timely reviews and constructive comments. AbstractIn ironic texts what is literally said is usually negated, and in absence of an explicit negation marker. This makes social computing quite challenging. Detecting irony is very much important for NLP tasks such as polarity classification, sentiment analysis, opinion mining, or reputation analysis. There is a growing interest from the research community in investigating the impact of irony on polarity classification and sentiment analysis. A tasks will be organised at SemEval in 2015 on Sentiment Analysis of Figurative Language in Twitter (http://alt.qcri.org/semeval2015/task11). What are the linguistic patterns that users employ in social media in order to try to be ironic in just maybe 140 characters? Linguistic devices that go beyond positive or negative polarity such as ambiguity, incongruity, unexpectedness and emotional contexts have an important role as triggers of irony. In the talk I will describe how irony is employed in social media texts (Twitter, Amazon, Facebook etc.) and what are the recent stateof-the-art attempts for its automatic detection. At the end of the talk, I will address also the even more challenging and fine-grained problem of distinguishing among irony, sarcasm and satire: e.g. If you find it hard to laugh at yourself, I...

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“…al. [14] had used machine learning algorithms for analysis sentiment of users and also introduced the technique for personal health information on twitter. Tanveer Ali et.…”

Section: Introductionmentioning

confidence: 99%

Patient Text Feedback Based Optimized Deep Learned Model to Identify the Impact of Therapy

Jagriti*¹,

Khullar²,

singh³

et al. 2019

IJITEE

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Text classification or Text mining is a very demanding field because the content created by the user in natural language is not easily understandable. It becomes very important to systematically identify and extract subjective information from user content so that it can be easily understandable. The whole process is done by assigning a particular class to text. In the field of opinion mining, most of the work has been done in common areas like restaurants, electronic goods, movie feedback, etc. and a lot of work needs to be done in the area of healthcare and medical. So, the proposed work has been carried over healthcare. The aim of this study is to classify the text feedback of patient using optimized deep learning model to identify the impact of therapy. In proposed method comparison of CNN with machine learning algorithms has been done, in which, CNN gave better results in terms of accuracy (99.98%), precision (0.981), recall (0.981), mean squared error (0.282). Further, we have implemented the CNN with N-gram technique and found that this method improved the results of CNN based on precision (0.999), recall (0.999), mean squared error (0.001), area under curve (0.998) but accuracy remained the same.

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Learning Sentiments from Tweets with Personal Health Information

Cited by 23 publications

References 13 publications

Sentiment and Factual Transitions in Online Medical Forums

Sentiment and Factual Transitions in Online Medical Forums

Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)

Patient Text Feedback Based Optimized Deep Learned Model to Identify the Impact of Therapy

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