Weighted Fuzzy Rule Based Sentiment Prediction Analysis on Tweets

Basha, Syed Muzamil; Rajput, Dharmendra Singh; N.Ch.S.N, Iyengar; Caytiles, Ronnie D.

doi:10.14257/ijgdc.2017.10.6.04

Cited by 20 publications

(7 citation statements)

References 9 publications

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“…Similarly, many other researchers used statistical techniques [30][31][32] and these methods are still very popular = because of newly invented statistical algorithms. Natural language processing has seen some usage [33,34] for predicting time series as well. Although not many researchers have applied natural language processing, the results that NLP produced was remarkable.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

A Review on Recent Advancements in FOREX Currency Prediction

Islam¹,

Hossain

Rahman³

et al. 2020

Algorithms

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In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.

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Section: Conceptual Frameworkmentioning

confidence: 99%

A Review on Recent Advancements in FOREX Currency Prediction

Islam¹,

Hossain

Rahman³

et al. 2020

Algorithms

View full text Add to dashboard Cite

show abstract

“…Extensive and informative extracts help improve machine learning productivity and reduce computational complexity. In this work, we employed vector space models [3], [4] to extract features from each file. Each blog post is displayed in vector form.…”

Section: A Feature Extraction Employing Vector Space Model Representmentioning

confidence: 99%

Intensity Weight Factor Based Sentiment Prediction Analysis on Tweets

K. N*.,

PADMA

2020

IJRTE

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Advances in the field of sentiment analysis are quick and purposeful to explore the views or articles available on various social media platforms through the techniques of machine learning with emotions, topic analysis or polarization calculations. Although employing various machine learning techniques and emotion analysis tools, there is a direct need for modern methods. To address these challenges, the contribution of this paper involves adopting a new approach that includes emotional analysts that integrates emotional intensity and machine learning. In addition, this document also provides a comparison of sentiment analysis techniques in analyzing political views through the application of machine learning algorithms such as Naive Bayes and KNN.

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“…Data mining algorithms are trained for selecting the model with high accuracy and performance of the proposed model is validated better using error metrics. In [14] the author make use of weighted fuzzy logic in initializing the exact weights to train the data in extracting sentiments from the labeled tweets. where as in [15] the author considered Time series dataset and measure the performance of predictive models.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Performance of Supervised Classification Models: Decision Tree and Naïve Bayes Using KNIME

Basha¹,

Rajput²,

Poluru³

et al. 2018

IJET

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The classification task is to predict the value of the target variable from the values of the input variables. If a target is provided as part of the dataset, then classification is a supervised task. It is important to analysis the performance of supervised classification models before using them in classification task. In our research we would like to propose a novel way to evaluated the performance of supervised classification models like Decision Tree and Naïve Bayes using KNIME Analytics platform. Experiments are conducted on Multi variant dataset consisting 58000 instances, 9 columns associated specially for classification, collected from UCI Machine learning repositories (http://archive.ics.uci.edu/ml/datasets/statlog+(shuttle)) and compared the performance of both the models in terms of Classification Accuracy (CA) and Error Rate. Finally, validated both the models using Metric precision, recall and F-measure. In our finding, we found that Decision tree acquires CA (99.465%) where as Naïve Bayes attain CA (90.358%). The F-measure of Decision tree is 0.984, whereas Naïve Bayes acquire 0.7045.

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Weighted Fuzzy Rule Based Sentiment Prediction Analysis on Tweets

Abstract: As E-Commerce is becoming more popular, the number of product reviews that a product received grows exponentially.

Cited by 20 publications

References 9 publications

A Review on Recent Advancements in FOREX Currency Prediction

A Review on Recent Advancements in FOREX Currency Prediction

Intensity Weight Factor Based Sentiment Prediction Analysis on Tweets

Evaluating the Performance of Supervised Classification Models: Decision Tree and Naïve Bayes Using KNIME

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