An Approach for Big Data to Evolve the Auspicious Information from Cross-Domains

Users of every system expect it to get better. Providing feedback to the owners or management was difficult but with the advent of technology, it has become handy. Users can now post their comments through online blogs, android apps and websites. Due to the enormous data piling up every second causes a problem in analyzing it. In this paper, sentiment analysis is used for analyzing comments and reviews for hospital management system are demonstrated with real time data. The tools, algorithms and methodology that could fetch accurate results is described. Experimental results indicate 90% of accuracy in proposed system. The review report generated would help the hospital management to identify the positive and negative feedback which further assists them in improving their facilities that could not only create customer satisfaction but also enhanced business processes.

Section: Sentiment Analysismentioning

confidence: 99%

Heart rate Encapsulation and Response Tool using Sentiment Analysis

Prabadevi

Reddy

Deepa

2019

“…They are pioneers for extracting, transforming & tagging popular movie review dataset [17].Domain specific sentiment analysis models were designed for various domains such as movie, restaurant, mobile, books and DVD's. The movie domain was relatively difficult to classify [18]. Top ranked index terms were not the top ranked sentimentally polarized terms.…”

Section: Introductionmentioning

confidence: 99%

Conceptual Sentiment Analysis Model

Ghag¹,

Shah²

2018

<span>Bag-of-words approach is popularly used for Sentiment analysis. It maps the terms in the reviews to term-document vectors and thus disrupts the syntactic structure of sentences in the reviews. Association among the terms or the semantic structure of sentences is also not preserved. This research work focuses on classifying the sentiments by considering the syntactic and semantic structure of the sentences in the review. To improve accuracy, sentiment classifiers based on relative frequency, average frequency and term frequency inverse document frequency were proposed. To handle terms with apostrophe, preprocessing techniques were extended. To focus on opinionated contents, subjectivity extraction was performed at phrase level. Experiments were performed on Pang & Lees, Kaggle’s and UCI’s dataset. Classifiers were also evaluated on the UCI’s Product and Restaurant dataset. Sentiment Classification accuracy improved from 67.9% for a comparable term weighing technique, DeltaTFIDF, up to 77.2% for proposed classifiers. Inception of the proposed concept based approach, subjectivity extraction and extensions to preprocessing techniques, improved the accuracy to 93.9%.</span>

“…Arora et al (12) proposed Cross BOMEST, a cross domain sentimental classification. Existing method BOMEST, it retrieves +ve words from a content, followed by determination of +ve word with assistance of Ms Word Introp.…”

Section: Introductionmentioning

confidence: 99%

Opinion mining on newspaper headlines using SVM and NLP

Rameshbhai

Paulose

2019

<p>Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.</p>