Twitter part-of-speech tagging using pre-classification Hidden Markov model

Sun, Shichang; Liu, Hongbo; Lin, Hongfei; Abraham, Ajith

doi:10.1109/icsmc.2012.6377881

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…A Twitter Sentiment Analysis for Cloud Providers: A Case Study of Azure -2016 (Qaisi and Aljarah, 2016) Naive Bayes AWS and Azure. The Opinion of customers around each one of them Shichang Sun and his team (Sun et al, 2012), used the classifier to classify the tweets as positive, negative and neutral, which uses POS (Parts of Speech) tagging for pre-processing of the twitter data. POS tagger is invoked as a pre-processing method is used in NLP program for information retrieval and extraction.…”

Section: Sentiword Netmentioning

confidence: 99%

Sentiment Analysis of Amazon Products Using Ensemble Machine Learning Algorithm

Sadhasivam

Kalivaradhan

2019

Int J Math, Eng, Manag Sci

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In recent years, Sentimental Analysis is used in all online product firms. The number of users using the particular product has increased which makes the industry to improvise the characteristics of the product. These days, many users who are using websites, blogs, online shopping tends to review the products they used. These reviews were taken into consideration by other users during their search for products. Hence the industry has found the root of delivering the correct product searched by the user based on the reviews of the users using the concept of sentimental analysis. Sentimental Analysis is the concept of data analysis where the collections of reviews are taken into consideration, and those reviews are analyzed, processed and recommended to the user. The reviews given are longer and which consist of a few paragraphs of content. In this paper, the dataset is collected from the official product sites. Initially, these reviews must be pre-processed in order to remove the unwanted data’s such as stop words, be verbs, punctuations, and conjunctions. Once, the pre-processing is over the trained dataset is classified using Naive Bayes and SVM algorithm. These existing algorithms provided the accuracy which is not worth enough. Hence, an ensemble approach has been applied to enhance the accuracy of the given reviews. An ensemble is a classification approach by combining two or more algorithms and calculate the mode value based on the vote reference for every algorithm which is used. In this paper, Naive Bayes, SVM, and Ensemble algorithm are combined. We proposed an Ensemble method that helps in providing better accuracy than the current existing algorithm. Once the accuracy is calculated, based on the reviews, the particular product is recommended for the user.

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Section: Sentiword Netmentioning

confidence: 99%

Sentiment Analysis of Amazon Products Using Ensemble Machine Learning Algorithm

Sadhasivam

Kalivaradhan

2019

Int J Math, Eng, Manag Sci

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“…This assumption is similar to the assumption of localized likelihoods and is called the 1st order Markov Property with respect to data. HMM has found applications in computational biology (e.g., Krogh et al 1994;Liang et al 2007;Pachter et al 2002;Shih et al 2015;Yoon 2009), natural language processing (e.g., Collins 2002;Nivre 2002;Sun et al 2012), speech recognition (e.g., Dymarski 2011; Rabiner 1989), computer vision (e.g., Li et al 2000;Othman & Aboulnasr 2003;Baumgartner et al 2013), earthquake seismology (e.g., Alasonati et al 2006;Beyreuther & Wassermann 2008;Can et al 2014), petroleum geoscience (e.g., Eidsvik et al 2004;Lindberg & Omre & 2015) and many other fields of research. all of these approaches for facies inversion is that they are based on inference from full posterior distribution which must be explored through simulation (sampling) based inference, e.g., using McMC methods which suffer from the convergence and bias problems described earlier.…”

Section: Hidden Markov Chain (1d-hmm)mentioning

confidence: 99%

Bayesian inversion of seismic attributes for geological facies using a Hidden Markov Model

Nawaz¹,

Curtis²

2016

Geophys. J. Int.

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Markov chain Monte-Carlo (McMC) sampling generates correlated random samples such that their distribution would converge to the true distribution only as the number of samples tends to infinity. In practice, McMC is found to be slow to converge, convergence is not guaranteed to be achieved in finite time, and detection of convergence requires the use of subjective criteria. Although McMC has been used for decades as the algorithm of choice for inference in complex probability distributions, there is a need to seek alternative approaches, particularly in high dimensional problems. Walker & Curtis (2014) developed a method for Bayesian inversion of two-dimensional spatial data using an exact sampling alternative to McMC which always draws independent samples of the target distribution. Their method thus obviates the need for convergence and removes the concomitant bias exhibited by finite sample sets. Their algorithm is nevertheless computationally intensive and requires large memory. We propose a more efficient method for Bayesian inversion of categorical variables, such as geological facies that requires no sampling at all. The method is based on a 2D Hidden Markov Model (2D-HMM) over a grid of cells where observations represent localized data constraining each cell. The data in our example application are seismic attributes such as P-and S-wave impedances and rock density; our categorical variables are the hidden states and represent the geological rock types in each cell-facies of distinct subsets of lithology and fluid combinations such as shale, brine-sand and gas-sand. The observations at each location are assumed to be generated from a random function of the hidden state (facies) at that location, and to be distributed according to a certain probability distribution that is independent of hidden states at other locations-an assumption referred to as localized likelihoods. The hidden state (facies) at a location cannot be determined solely by the observation at that location as it also depends on prior information concerning the spatial distribution of other hidden states elsewhere. The prior information is included in the inversion in the form of a training image which represents a conceptual depiction of local geologies that might be expected, but other forms of prior information can be used in the method as desired. The method provides direct estimates of posterior marginal probability distributions over each

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“…Examples of unsupervised learning approach include K-means clustering, hidden Markov models etc. [13][14]. Semi Supervised Learning uses a combination of machine learning and lexicon based approaches.…”

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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Twitter part-of-speech tagging using pre-classification Hidden Markov model

Cited by 8 publications

References 17 publications

Sentiment Analysis of Amazon Products Using Ensemble Machine Learning Algorithm

Sentiment Analysis of Amazon Products Using Ensemble Machine Learning Algorithm

Bayesian inversion of seismic attributes for geological facies using a Hidden Markov Model

Sentiment analysis from social media in crisis situations

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