Evaluating Sentiment Analysis Methods and Identifying Scope of Negation in Newspaper Articles (original) (raw)

IRJET-Sentiment Analysis of Political News articles and the effect of negation scope

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.

Negation handling for sentiment analysis task: approaches and performance analysis

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.

Document Level Sentiment Analysis from News Articles

Now a days, huge amount data has generated on the internet and it is important to extract useful information from that huge data. Different data mining techniques are used to extract and implement to solve divers types of problems. In the era of News and blogs, there is need to extract news and need to analyze to determine opinion of that news reviews. Sentiment analysis finds an opinion i.e. positive or negative about particular subject. Negation is a very common morphological creation that affects polarity and therefore, needs to be taken into reflection in sentiment analysis. Automatic detection of negation from News article is a need for different types of text processing applications including Sentiment Analysis and Opinion Mining. Our system uses online news databases from one resources namely BBC news. While handling news articles, we executed three subtasks namely categorizing the objective, separation of good and bad news content from the articles and performed preprocessing of data is cleaned to get only what is required for analysis, Steps like tokenization, stop word removal etc. The currently work focuses on different computational methods modeling negation in sentiment analysis. Especially, on aspects level of representation used for sentiment analysis, negation word recognition and scope of negation and identification.

A Survey on Various Negation Handling Techniques in Sentiment Analysis

Smart and Sustainable Intelligent Systems, 2021

Sentiment Analysis or opinion mining alludes to the way toward deciding sentiments or feelings communicated in content about a subject. But this sentiment changes with the presence of other constructs in the sentence. Negation is one such construct which either flips the sentiment or increase or decrease the intensity of the sentiment. This paper is an attempt to study the work done by the different researchers for identification of the negation cues and their scope. Paper is organized according to the different feature selection methods employed and how different researchers contributed to this. Various algorithms proposed by the researchers were presented. We have also identified the shortcomings of the various studies which form the basis for further development using other techniques like deep learning.

The Development of Indonesian Sentiment Analysis with Negation Handling

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.