Sentiment Analysis of Stock Market News with Semi-supervised Learning

Mizumoto, Keisuke; Yanagimoto, Hidekazu; Yoshioka, Michifumi

doi:10.1109/icis.2012.97

Cited by 22 publications

(7 citation statements)

References 5 publications

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“…Bayesian model is used for classification. Authors in [19] proposed construction of dictionary for sentiment analysis using semi-supervised learning approach. Stock market news dataset is used for sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Towards the Exploitation of Statistical Language Models for Sentiment Analysis of Twitter Posts

Bhattacharya

Banerjee

2017

Computer Information Systems and Industrial Management

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Part 4: Engineering of Enterprise Software ProductsInternational audienceIn this paper, we investigate the utility of linguistic features for detecting the sentiment of twitter messages. The sentiment is defined to be a personal positive or negative feelings. We built n-gram language models over zoos of positive and negative tweets. We assert the polarity of a given tweet by observing the perplexity with the positive or negative language model. The given tweet is considered to be close to the language model that assigns lower perplexity

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Section: Related Workmentioning

confidence: 99%

Towards the Exploitation of Statistical Language Models for Sentiment Analysis of Twitter Posts

Bhattacharya

Banerjee

2017

Computer Information Systems and Industrial Management

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“…Semi Supervised Learning uses a combination of machine learning and lexicon based approaches. Examples of semi supervised approaches include graph based methods, heuristic approaches [15].…”

Section: A General Sentiment Analysismentioning

confidence: 99%

Sentiment analysis from social media in crisis situations

Kaur

Kumar

2015

International Conference on Computing, Communication &Amp; Automation

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Advent of social media has created an unprecedented environment for people to share their thoughts with the world. These online platforms like facebook, twitter are usually the first resort people turn to in times of crisis to voice their opinions and relay other crucial information. But when it comes to detecting sentiments out of this gigantic pool of opinions, it becomes an arduous task and doing it manually is practically impossible. Hence different methods have been devised to perform automatic polarity classification of textual data. This paper provides a brief overview of different techniques being developed for analyzing social media data, particularly twitter data. We developed a workflow for applying sentiment analysis to a comparatively new domain of natural disasters to detect public emotions in crisis. A base line model is designed and trained on unigram features using Naïve Bayes. The model is further tested on Kashmir floods dataset collected from twitter and an overall accuracy of 67% is achieved. The result provides valuable information which will assist the authorities to strategize their actions with due consideration to public sentiments and hence ameliorate the process of managing such situations.

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“…Mizumoto et al [27] proposed the system in which the author created polarity dictionary to determine the sentimental polarities of stock market. While constructing the dictionary, the author has used the semi-supervised learning approach from which small polarity dictionary is made.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Mizumoto et al [27] used semi-supervised learning for sentiment analysis. The technique created the polarity dictionary to estimate the polarity of stock market contextual information.…”

Section: Critical Analysismentioning

confidence: 99%

A Study of Sentiment and Trend Analysis Techniques for Social Media Content

Mehmood¹,

Palli²,

Khan³

2014

IJMECS

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The social media networks have evolved rapidly and people frequently use these services to communicate with others and express themselves by sharing opinions, views, ideas etc. on different topics. The social media trend analysis is generally carried out by sifting the corresponding or interlinked events discussed on social media websites such as Twitter, Facebook etc. The fundamental objective behind such analyses is to determine the level of criticality with respect to criticism or appreciation described in the comments, tweets or blogs. The trend analysis techniques can also be systematically exploited for opinion making among the masses at large. The results of such analyses show how people think, assess, orate and opine about different issues. This paper primarily focuses on the trend detection and sentiment analysis techniques and their efficacy in the contextual information. We further discuss these techniques which are used to analyze the sentiments expressed within a particular sentence, paragraph or document etc. The analysis based on sentiments can pave way for automatic trend analysis, topic recognition and opinion mining etc. Furthermore, we can fairly estimate the degree of positivity and negativity of the opinions and sentiments based on the content obtained from a particular social media.

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Sentiment Analysis of Stock Market News with Semi-supervised Learning

Cited by 22 publications

References 5 publications

Towards the Exploitation of Statistical Language Models for Sentiment Analysis of Twitter Posts

Towards the Exploitation of Statistical Language Models for Sentiment Analysis of Twitter Posts

Sentiment analysis from social media in crisis situations

A Study of Sentiment and Trend Analysis Techniques for Social Media Content

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