scholarly journals Sentiment Analysis using Feature Based Support Vector Machine – A Proposed Method

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3671-3676 ◽  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Vital Role ◽  
Support Vector ◽  
Features Selection ◽  
Linguistic Features ◽  
Good Tool ◽  
Business Decisions ◽  
The People ◽  
Feature Based

Business decisions for any service or product depend on sentiments by the people. The mood of people towards any event, service and product are expressed in sentiments. The text sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments.The features like duplication of sentiment, unknown emotics may change the polarity of sentiment.If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Support vector machine proved as a good tool of machine learning for better sentiment analysis.Better use of parts os speech (POS) folled by guided preprocessing and evaluation will provide less errorus polarity of sentiments

scholarly journals Business recommendation based on collaborative filtering and feature engineering – aproposed approach

2019 ◽  
Vol 9 (4) ◽  
pp. 2614
Author(s):  
Prakash Pandharinath Rokade ◽  
Aruna Kumari D
Keyword(s):  
Collaborative Filtering ◽  
Vital Role ◽  
Learning Approach ◽  
Features Selection ◽  
Business Analytics ◽  
Linguistic Features ◽  
Business Decisions ◽  
Parts Of Speech ◽  
Machine Learning Approach ◽  
Feature Based

Business decisions for any service or product depend on sentiments by people. We get these sentiments or rating on social websites like twitter, kaggle.  The mood of people towards any event, service and product are expressed in these sentiments or rating. The text of sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments. If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Feature based collaborative filtering can be used for better sentiment analysis. Better use of parts of speech (POS) followed by guided preprocessing and evaluation will minimize error for sentiment polarity and hence the better recommendation to the user for business analytics can be attained.

scholarly journals Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 16 ◽  
Author(s):  
Sattam Almatarneh ◽  
Pablo Gamallo
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Empirical Study ◽  
Learning Strategies ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Word Embeddings ◽  
Linguistic Features ◽  
Machine Learning Classifiers ◽  
Supervised Machine Learning Classifiers

In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised machine learning classifiers: Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM). The experiments we have carried out show that SVM clearly outperforms NB and DT in all datasets by taking into account all features individually as well as their combinations.

scholarly journals Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions

2018 ◽  
Author(s):  
Sattam Almatarneh ◽  
Pablo Gamallo
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Empirical Study ◽  
Learning Strategies ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Word Embeddings ◽  
Linguistic Features ◽  
Machine Learning Classifiers ◽  
Supervised Machine Learning Classifiers

In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised machine learning classifiers: Support Vector Machine (SVM), Naive Bayes (NB), and Decision Tree (DT).

scholarly journals An Enhanced Approach for Sentiment Analysis Using Association Rule Mining

2021 ◽  
Vol 9 (12) ◽  
pp. 913-918
Author(s):  
Abhishek Sharma
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Opinion Mining ◽  
Support Vector ◽  
Rule Mining ◽  
The People ◽  
Baseline Approach

Abstract: In today’s world social networking platforms like Facebook, YouTube, twitter etc. are a great source of communication for internet users and loaded with large number of emotions, views and opinions of the people. Sentiment analysis is the study of attitudes, emotions and opinions of the people and is also known as opinion mining. Sentiment analysis is used to find the opinion i.e. negative or positive about a particular subject. In this paper an Enhanced sentiment analysis approach is presented by using the Association rule mining i.e. Apriori and machine learning approach such as Support Vector Machine. The Enhanced approach is compared with the baseline approach, on accuracy, precision, recall, and F1-score measures. The Enhanced approach for sentiment analysis is implemented using the R programming language. The Enhanced approach shows better performance in comparison to the baseline approach. Keyword: Sentiment Analysis, Opinion Mining, Support Vector Machine, Association Rule Mining, Machine Learning

scholarly journals A Comparison of Machine Learning Techniques for Sentiment Analysis

2021 ◽  
Vol 12 (3) ◽  
pp. 1738-1744
Author(s):  
Shahzad Qaiser Et.al
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Modern Method ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
The People ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Single Dataset

The availability of the data has increased tremendously due to the excess usage of social media platforms like Twitter and Facebook. Due to the abundant availability of data, scientists, businesses, educationalists and other people working under different roles have started using Sentiment Analysis (SA) to get in-depth knowledge about the sentiments of the people regarding any topic of interest. There are many techniques to implement SA, and one of them is Machine Learning (ML). This study is focused on the comparison of ancient ML methods such as Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and a modern method, i.e., Deep Learning (DL). The ML techniques are applied to a single dataset to compare their performance in terms of accuracy to understand how they perform against each other. The study found that DL performed the best with 96.41% accuracy followed by NB and SVM with 87.18% and 82.05% respectively. DT performed the poorest with 68.21% accuracy.

scholarly journals You, thou and thee

2019 ◽  
Vol 59 (1) ◽  
Author(s):  
Isolde Van Dorst
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Conversation Analysis ◽  
Support Vector ◽  
Linguistic Context ◽  
Statistical Machine Learning ◽  
Linguistic Features ◽  
Learning Biases ◽  
The Status ◽  
Negative Sentiment

This study creates a prediction model to identify which linguistic and extra-linguistic features influence pronoun choices in the plays of Shakespeare. In the English of Shakespeare’s time, the now-archaic distinction between you and thou persisted, and is usually reported as being determined by relative social status and personal closeness of speaker and addressee. But it remains to be determined whether statistical machine learning will support this traditional explanation. 23 features are investigated, having been selected from multiple linguistic areas, such as pragmatics, sociolinguistics and conversation analysis. The three algorithms used, Naive Bayes, decision tree and support vector machine, are selected as illustrative of a range of possible models in light of their contrasting assumptions and learning biases. Two predictions are performed, firstly on a binary (you/thou) distinction and then on a trinary (you/thou/thee) distinction. Of the three algorithms, the support vector machine models score best. The features identified as the best predictors of pronoun choice are the words in the direct linguistic context. Several other features are also shown to influence the pronoun prediction, including the names of the speaker and addressee, the status differential, and positive and negative sentiment.

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

Sign in / Sign up

Export Citation Format

Share Document