Sentiment analysis: A literature review

Nanli, Zhu; Ping, Zou; Li, Weiguo; Meng, Cheng Ee

doi:10.1109/ismot.2012.6679538

Cited by 50 publications

(21 citation statements)

References 20 publications

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“…Naïve Bayes method is a machine learning probabilistic approach [24]. Naïve Bayes method falls in the category of supervised learning [25]. This method uses strong dependence features between words and creates a graph where nodes and edges represent random variables and dependencies respectively.…”

Section: Naïve Bayes Methodsmentioning

confidence: 99%

Emotion detection on cross platform languages

Arif

Singh

Boparai

2018

IJET

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Internet has changed the course of our living. It has become the most beneficial antecedent or source of information. Today almost everything is found on internet. Everyday millions of people post their ideas, reviews, stories about the services, products or other persons. The size of data is increasing tremendously. It is very difficult to analyze that amount of data and figure out the emotions or sentiments posed by people. Emotion detection is such a technique where we can judge people’s ideas and extract the emotion towards an entity or service. We have used subjective lexicon-based approach to bench the emoticons expressed by the ideas of the people. The data set that we have mainly focused is very cross and noisy. We have used Facebook data in Urdu and Kashmiri language. Both languages are very cross domain. These languages can be written in English alphabet that makes them more challenging to analyses. Our approach resolves the challenge to the maximum possible way. The results shown by our method on this kind of data set are better than any other approach. Our analysis on this type of dataset will help the local businessmen of these areas to grow and flourish. The analysis will give some insights what the local feel about the entity or product so that the manufacturers can design or build it that way and try to enhance its qualities.

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Section: Naïve Bayes Methodsmentioning

confidence: 99%

Emotion detection on cross platform languages

Arif

Singh

Boparai

2018

IJET

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“…SA or opinion mining aims at understanding a user's attitude and opinions by investigating, analysing and extracting subjective texts involving users' opinions, preferences and sentiment [13]. It is a sophisticated process that not only involve with the model training, but also a numerous additional procedure such as data processing, transformation and dimensionality reduction [14].…”

Section: Related Workmentioning

confidence: 99%

Predicting Supervise Machine Learning Performances for Sentiment Analysis Using Contextual-Based Approaches

Aziz

Starkey

2020

IEEE Access

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Sentiment Analysis (SA) is focused on mining opinion (identification and classification) from unstructured text data such as product reviews or microblogs. It is widely used for brand reviews, political campaigns, marketing analysis or gaining feedback from customers. One of the prominent approaches for SA is using supervised machine learning (SML), an algorithm that uses datasets with defined class labels based on mathematical learning from a training dataset. While the results are promising especially with indomain sentiments, there is no guarantee the model provides the same performance against real time data due to the diversity of new data. In addition, previous studies suggest the result of SML decrease when applied to cross-domain datasets because new features are appeared in different domains. So far, studies in SA emphasise the improvement of the sentiment result whereas there is little discussion focusing on how to detect the degradation of performance for the proposed model. Therefore, we provide a method known as Contextual Analysis (CA), a mechanism that constructs a relationship between words and sources that is constructed in a tree structure identified as Hierarchical Knowledge Tree (HKT). Then, Tree Similarity Index (TSI) and Tree Differences Index (TDI), a formula generate from tree structure are proposed to find similarity as well as changes between train and actual dataset. The regression analysis of datasets reveals that there is a highly significant positive relationship between TSI and SML accuracies. As a result, the prediction model created indicated estimation error within 2.75 to 3.94 and 2.30 for 3.51 for average absolute differences. Moreover, this method also can cluster sentiment words into positive and negative without having any linguistics resources used and at the same time capturing changes of sentiment words when a new dataset is applied.INDEX TERMS Text analytics, sentiment analysis, contextual analysis, supervised machine learning.

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“…Here, sentiment analysis was done on the famous social media, Twitter where the data was downloaded for the analysis. Nowadays, Sentiment Analysis is an emerging trend as a lot of organizations or institutions following this procedure to understand the views and opinions of various people [2] . For example, the usage of a particular product can be analyzed by the way people respond to it [1].…”

Section: Sentiment Analysismentioning

confidence: 99%

Multiclass Sentiment Analysis of Social Media Data using Neural Networks

Babu¹,

Rawther²

2019

IJEAT

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Sentiment analysis, also known as Opinion Mining is one of the hottest topic Nowadays. in various social networking sites is one of the hottest topic and field nowadays. Here, we are using Twitter, the biggest web destinations for people to communicate with each other to perform the sentiment analysis and opinion mining by extracting the tweets by various users. The users can post brief text updates in twitter as it only allows 140 characters in one text message. Hashtags helps to search for tweets dealing with the specified subject. In previous researches, binary classification usually relies on the sentiment polarity(Positive , Negative and Neutral). The advantage is that multiple meaning of the same world might have different polarity, so it can be easily identified. In Multiclass classification, many tweets of one class are classified as if they belong to the others. The Neutral class presented the lowest precision in all the researches happened in this particular area. The set of tweets containing text and emoticon data will be classified into 13 classes. From each tweet, we extract different set of features using one hot encoding algorithm and use machine learning algorithms to perform classification. The entire tweets will be divided into training data sets and testing data sets. Training dataset will be pre-processed and classified using various Artificial Neural Network algorithms such as Reccurent Neural Network, Convolutional Neural Network etc. Moreover, the same procedure will be followed for the Text and Emoticon data. The developed model or system will be tested using the testing dataset. More precise and correct accuracy can be obtained or experienced using this multiclass classification of text and emoticons. 4 Key performance indicators will be used to evaluate the effectiveness of the corresponding approach.

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Sentiment analysis: A literature review

Cited by 50 publications

References 20 publications

Emotion detection on cross platform languages

Emotion detection on cross platform languages

Predicting Supervise Machine Learning Performances for Sentiment Analysis Using Contextual-Based Approaches

Multiclass Sentiment Analysis of Social Media Data using Neural Networks

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