Document Level Sentiment Analysis from News Articles

With the explosion of Web and increased activity in blogging, tagging and commenting, there has been an eruption of interest in people to mine these vast resources for opinions. Sentiment Analysis or Opinion Mining is the computational treatment of opinions, sentiments and subjectivity of text. In this paper, we present our work on analyzing public sentiment towards two major political parties namely Congress and BJP using online news articles(formal text) and user comments on these articles(informal text). While informal texts express the opinion directly, formal texts express the opinion in a subtle way. In order to extract the sentiment from both informal and formal texts in an effective way, we have used standard lexical resource-SentiWordNet for classifying the text as positive or negative or neutral. However, without effective calculation of negation and its scope, sentiment calculation will not be accurate. Handling negation and determining the scope of negation is a challenging task in sentiment analysis but has still received little attention. We present an effective method to calculate negation scope and draw a comparison with widely used negation scope determination algorithms.

International Journal of Electrical and Computer Engineering (IJECE), 2024

Negation plays an essential role in sentiment analysis within natural language processing (NLP). Its integration involves two key aspects: identifying the scope of negation and incorporating this information into the sentiment model. Before delving into scope detection, the specific negation cue must be identified, with explicit and implicit negation cues being the two main types. Various methodologies, such as rule-based, machine learning, and hybrid approaches, address the negation scope detection challenge. Strategies for leveraging negation information in sentiment models encompass heuristic polarity modification, feature space augmentation, endto-end approach, and hierarchical multi-task learning. Notably, there is a need for more studies addressing implicit negation cue detection, even within the state-of-the-art bidirectional encoder representation for transformers (BERT) approach. Some studies have employed reinforcement learning and hybrid techniques to address the implicit negation problem. Further exploration, particularly through a hybrid and multi-task learning approach, is warranted to make potential contributions to the nuanced challenges of handling negation in sentiment analysis, especially in complex sentence structures.

2020

The polarity of a text can be seen based on the words used in the text. The use of negation words in a sentiment can change the polarity of the sentiment. One example of a negation word in Indonesian is the word 'tidak'. If a sentiment has a word “tidak” combined with a word “jelek” to form “tidak jelek”, then the sentiment is classified into positive class. However, if the combination of the word “tidak jelek” is processed as a word that has no relationship, then the sentiment will be falsely classified into negative class. The existence of negation word in a sentiment is one of the challenges in sentiment analysis, because it can cause ambiguity and misclassification when classifying text based on its polarity. This research shows that negation handling cannot be done only by doing syntactic analysis, but it also requires semantic analysis to overcome the problem of ambiguity.

International journal of computing and digital system/International Journal of Computing and Digital Systems, 2024

Negation is one of the challenges in sentiment analysis. Negation has an immense influence on how accurately text data can be classified. To find accurate sentiments of users this research identifies that the impact of negations in a sentence needs to be properly handled. Traditional approaches are unable to properly determine the scope of negations. In the proposed approach Machine learning (ML) is used to find the scope of negations. Moreover, the removal of negative stopwords during pre-processing leads to the flipped polarity of sentences. To resolve these challenges this research proposes a method for negation scope detection and handling in sentiment analysis. First, negation cue (negative words) and non cue words are determined, these negation cue and non cue words in addition to lexical and syntactic features determine the negation scope (part of sentence affected by cue) using the Machine Learning (ML) approach i.e. Conditional Random Fields (CRF). Subsequently, in negation handling the sentiment intensity of each token in a sentence is established, and affected tokens are processed to determine the final polarity. It is revealed that sentiment analysis with negation handling and calculated polarity gives 3.61%, 2.64%, 2.7%, and 1.42% increase in accuracy for Logistic regression, Support Vector Machine, Decision Tree (DT), and Naive Bayes (NB) consecutively for Amazon food products dataset. Consecutively, 9.4%, 3%, and 2% improvement for Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes for electronic dataset.

Now a day's sentiment analysis performs a very vital role in text mining. In essence web mining is a very broad area in a data mining field for extracts the sentiment of the text. To identify the sentiment of the textual data is a very challenging task. The present work focuses on sentence level negation identification and calculation from the News articles and Blogs. Two step approaches generally used for analysis namely preprocessing and post processing. Preprocessing consists of the tasks like stop word removing, punctuation mark removal, number removal, white space removal etc. Post processing comprises identification of sentiments from the text and calculation of score. The work analyses the performance of support vector machine, Naïve Bayes for the dataset collected online.

International Journal of Healthcare Information Systems and Informatics

Negation is an important linguistic phenomenon that needs to be considered for identifying correct sentiments from the opinionated data available in digital form. It has the power to alter the polarity or strength of the polarity of affected words. In this paper, the authors present a survey on the negation role that has been done until now in sentiment analysis, specifically Twitter sentiment analysis. The authors discuss the various approaches of modelling negation in Twitter sentiment analysis. In particular, their focus is on negation scope detection and negation handling methods. This article also presents some of the challenges and limits of negation accounting in the field of Twitter sentiment analysis.

Sentiment analysis is a widely used phenomenon for analyzing online user responses to infer collective response and it is used in various applications. Negation is a very common morphological creation that affects polarity. This research paper focuses on sentence level negation identification from news articles this work uses online news articles Data from BBC news. Results are analyzed using Machine Learning Algorithms like Support vector Machine and Naïve Bayes. Support Vector Machine achieves 96.46% accuracy and Naive Bayes achieves 94.16%.

Advances in Intelligent and Soft Computing, 2011

As virtual utterances of opinions or sentiment are becoming increasingly abundant on the Web, automated ways of analyzing sentiment in such data are becoming more and more urgent. In this paper, we provide a classification scheme for existing approaches to document sentiment analysis. As the role of negations in sentiment analysis has been explored only to a limited extent, we additionally investigate the impact of taking into account negation when analyzing sentiment. To this end, we utilize a basic sentiment analysis framework -consisting of a wordbank creation part and a document scoring part -taking into account negation. Our experimental results show that by accounting for negation, precision on human ratings increases with 1.17%. On a subset of selected documents containing negated words, precision increases with 2.23%.

The presence of the word negation is able to change the polarity of the text if it is not handled properly it will affect the performance of the sentiment classification. Negation words in Indonesian are 'tidak', 'bukan', 'belum' and 'jangan'. Also, there is a conjunction word that able to reverse the actual values, as the word 'tetapi', or 'tapi'. Unigram has shortcomings in dealing with the existence of negation because it treats negation word and the negated words as separate words. A general approach for negation handling in English text gives the tag 'NEG_' for following words after negation until the first punctuation. But this may gives the tag to un-negated, and this approach does not handle negation and conjunction in one sentences. The rule-based method to determine what words negated by adapting the rules of Indonesian language syntactic of negation to determine the scope of negation was proposed in this study. With adapting syntactic rules and tagging "NEG_" using SVM classifier with RBF kernel has better performance results than the other experiments. Considering the average F1-score value, the performance of this proposed method can be improved against baseline equal to 1.79% (baseline without negation handling) and 5% (baseline with existing negation handling) for a dataset that all tweets contain negation words. And also for the second dataset that has the various number of negation words in document tweet. It can be improved against baseline at 2.69% (without negation handling) and 3.17% (with existing negation handling).

Research in Computing Science, 2015

Sentiment Analysis (SA) is a challenging area where several problems still are open, one of them is treating the negation. Most of the research focusses on reviews written in English. Therefore, algorithms to deal with the negation in other languages are needed. This work proposes an algorithm to determine the scope of negative particles and to treat the affected words on reviews in Spanish. Our algorithm includes a novel weight scheme which combines the relative frequency of a word with the vocabulary of the class. The experiments show that handling the negation improves the accuracy in Spanish reviews in most cases. Likewise, the proposed weight scheme reaches a maximum accuracy of 67% before treating the negation. Combining our algorithm with the proposed weight scheme yields an accuracy greater than the one reported in the state of the art.