Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about

A Retrospective Analysis of the Fake News Challenge Stance Detection Task

The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods. In this paper, we provide such an in-depth analysis for the three top-performing systems. We first find that FNC-1's proposed evaluation metric favors the majority class, which can be easily classified, and thus overestimates the true discriminative power of the methods. Therefore, we propose a new F1-based metric yielding a changed system ranking. Next, we compare the features and architectures used, which leads to a novel feature-rich stacked LSTM model that performs on par with the best systems, but is superior in predicting minority classes. To understand the methods' ability to generalize, we derive a new dataset and perform both in-domain and cross-domain experiments. Our qualitative and quantitative study helps interpreting the original FNC-1 scores and understand which features help improving performance and why. Our new dataset and all source code used during the reproduction study are publicly available for future research

An Expert System Technique for Sentiment Analysis of Opinions

To help the users and the product owners it is quite necessary to extract aspects from the online reviews, their sentiment polarities, and associations between them. There is a great deal of work done in the field of sentiment analysis. Lexical and learning-based systems can be combined to separate the assessments from online opinions and reviews. In learning-based techniques, the Gaussian mixture model can be used for getting probabilistic results for polarities against aspects and naïve baize classifiers for the problem of spam comments which produced better and competitive results against previous techniques

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method

Social Networking sites have become popular and common places for sharing wide range of emotions through short texts. These emotions include happiness, sadness, anxiety, fear, etc. Analyzing short texts helps in identifying the sentiment expressed by the crowd. Sentiment Analysis on IMDb movie reviews identifies the overall sentiment or opinion expressed by a reviewer towards a movie. Many researchers are working on pruning the sentiment analysis model that clearly identifies and distinguishes between a positive review and a negative review. In the proposed work, we show that the use of Hybrid features obtained by concatenating Machine Learning features (TF, TF-IDF) with Lexicon features (Positive-Negative word count, Connotation) gives better results both in terms of accuracy and complexity when tested against classifiers like SVM, Naïve Bayes, KNN and Maximum Entropy. The proposed model clearly differentiates between a positive review and negative review. Since understanding the context of the reviews plays an important role in classification, using hybrid features helps in capturing the context of the movie reviews and hence increases the accuracy of classification

Conceptual Sentiment Analysis Model

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%

SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis

Sentiment Analysis has been a topic of interest for researchers due to its increasing usage by Industry. To measure end-user sentiment., there is no clear verdict on which algorithms are better in real-time scenarios. A rigorous benchmark evaluation of various algorithms running across multiple datasets and different hardware architectures is required that can guide future researchers on potential advantages and limitations. In this paper, proposed SentiMLBench is a critical evaluation of key ML algorithms as standalone classifiers, a novel cascade feature selection (CFS) based ensemble technique in multiple benchmark environments each using a different twitter dataset and processing hardware. The best trained ensemble model with CFS enhancement surpasses current state-of-the-art models, according to experimental results. In a study, though ensemble model provides good accuracy, it falls short of neural networks accuracy by 2%. ML algorithms accuracy is poor as standalone classifiers across all three studies. The supremacy of neural networks is further stamped in study three where it outperforms other algorithms in accuracy by over 10%. Graphical processing unit provide speed and higher computational power at a fraction of a cost compared to a normal processor thereby providing critical architectural insights into developing a robust expert system for sentiment analysis

Biocom_Usp: tweet sentiment analysis with adaptive boosting ensemble

We describe our approach for the SemEval-2014 task 9: Sentiment Analysis in Twitter. We make use of an ensemble learning method for sentimento classification of tweets that relies on varied features such as feature hashing, part-of-speech, and lexical features. Our system was evaluated in the Twitter message-level task.CAPESFAPESPCNP