Negation In Sentiment Analysis Research Articles

Contextual Ambiguity Framework for Enhanced Sentiment Analysis

Negation is a universal linguistic phenomenon that affects the performance of Natural Language Processing (NLP) applications, especially opinion mining data. Many words exists in sentences that have multiple interpretations or sentiments depending on how they are placed with respect to the negation word in the sentence. A cutting-edge framework is designed to tackle the nuanced challenge of detecting contextual ambiguity through negation in sentiment analysis. The approach uniquely combines advanced natural language processing techniques with deep linguistic insights, enabling the accurate interpretation of sentiment in complex sentences where negation plays a key role. The framework identifies negation cues and their scope, then assesses their impact on sentiment, considering contextual dependencies and word semantics. The model's innovation lies in context-sensitive algorithms that adeptly handle different sentence structures and idiomatic expressions, a notable advancement over traditional sentiment analysis tools. Particularly effective in interpreting sarcastic or ironic statements, the framework significantly outperforms existing models in accuracy, especially in negation-heavy contexts. This advancement enhances sentiment analysis applications like social media monitoring and customer feedback analysis, offering a more nuanced understanding of public opinion.

Negation handling for sentiment analysis task: approaches and performance analysis

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, end-to-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.

Negation Detection Techniques in Sentiment Analysis: A Survey

Negation is a linguistic phenomenon that can cause sentences to have their meanings reversed. It frequently inverts affirmative sentences into negative ones, affecting the polarity; therefore, the sentiment of the text also changes accordingly. Negation can be expressed differently, making it somewhat challenging to detect. As a result, detecting negation is critical for Sentiment Analysis (SA) system development and improvement and will increase classifier accuracy, but it also poses a significant conceptual and technical challenge. This paper aims to survey and gather the most recent research related to detecting negation in SA. Many researchers have worked and performed methods, including algorithmic, machine, and deep learning approaches such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayesian (NB), Logistic Regression (LR), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Bidirectional Long Short-Term Memory (BiLSTM), and other hybrid methods such as rule-based and machine learning, lexicon and machine learning, machine learning, and deep learning. It addresses and tries to identify the gaps in the current studies, laying the foundation for future studies in this field.

Negation detection techniques in sentiment analysis: A survey

Negation is a linguistic phenomenon that can cause sentences to have their meanings reversed. It frequently inverts affirmative sentences into negative ones, affecting the polarity; therefore, the sentiment of the text also changes accordingly. Negation can be expressed differently, making it somewhat challenging to detect. As a result, detecting negation is critical for Sentiment Analysis (SA) system development and improvement and will increase classifier accuracy, but it also poses a significant conceptual and technical challenge. This paper aims to survey and gather the most recent research related to detecting negation in SA. Many researchers have worked and performed methods, including algorithmic, machine, and deep learning approaches such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayesian (NB), Logistic Regression (LR), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Bidirectional Long Short-Term Memory (BiLSTM), and other hybrid methods such as rule-based and machine learning, lexicon and machine learning, machine learning and deep learning. In addition, this paper points out the gaps and future research directions in this area.

Improving the Performance of Sentiment Analysis Using Enhanced Preprocessing Technique and Artificial Neural Network

With the presence of a massive amount of digitally recorded data, an automated computation can be preferable over the manual approach to evaluate sentiments within given textual fragments. Artificial neural network (ANN) is preferred for sentiment analysis (SA) because of its learning ability and adaptive nature towards diverse data. Handling negation in SA is a challenging task, and to address the same, we propose a specific order of preprocessing (PPR) steps to enhance the performance of SA using ANN. Typically, ANN weights are randomly initialized (R-ANN), which may not give the desired performance. As a potential solution, we propose a novel approach named Matching features with output label based Advanced Technique (MAT) to initialize the ANN weights (MAT-ANN). Simulation results conclude the superiority of the proposed approach PPR+MAT-ANN compared to the existing approach EPR+R-ANN i.e., integrating existing preprocessing (EPR) steps with R-ANN. Moreover, PPR+MAT-ANN architecture is significantly simpler than the existing deep learning-based approach named the NeuroSent tool and gives better performance when evaluated upon the Dranziera protocol.

Improving sentiment analysis with multi-task learning of negation

AbstractSentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text.Negationis the most prevalent of these phenomena, and in order to correctly predict sentiment, a classifier must be able to identify negation and disentangle the effect that its scope has on the final polarity of a text. This paper proposes a multi-task approach to explicitly incorporate information about negation in sentiment analysis, which we show outperforms learning negation implicitly in an end-to-end manner. We describe our approach, a cascading and hierarchical neural architecture with selective sharing of Long Short-term Memory layers, and show that explicitly training the model with negation as an auxiliary task helps improve the main task of sentiment analysis. The effect is demonstrated across several different standard English-language data sets for both tasks, and we analyze several aspects of our system related to its performance, varying types and amounts of input data and different multi-task setups.

Automatic generation of lexica for sentiment polarity shifters

AbstractAlleviating pain is good and abandoning hope is bad. We instinctively understand how words like alleviate and abandon affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns, and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focused almost exclusively on a small handful of closed-class negation words, such as not, no, and without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources by introducing a large lexicon of polarity shifters that covers English verbs, nouns, and adjectives. Creating the lexicon entirely by hand would be prohibitively expensive. Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%. Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness. The created lexicon is evaluated both on a polarity shifter gold standard and on a polarity classification task.

Negation detection for sentiment analysis: A case study in Spanish

AbstractAccurate negation identification is one of the most important tasks in the context of sentiment analysis. In order to correctly interpret the sentiment value of a particular expression, we need to identify whether it is in the scope of negation. While much of the work on negation detection has focused on English, we have seen recent developments that provide accurate identification of negation in other languages. In this paper, we provide an overview of negation detection systems and describe an implementation of a Spanish system for negation cue detection and scope identification. We apply this system to the sentiment analysis task, confirming also for Spanish that improvements can be gained from accurate negation detection. The paper contributes an implementation of negation detection for sentiment analysis in Spanish and a detailed error analysis. This is the first work in Spanish in which a machine learning negation processing system is applied to the sentiment analysis task. Existing methods have used negation rules that have not been assessed, perhaps because the first Spanish corpus annotated with negation for sentiment analysis has only recently become available.

SFU ReviewSP-NEG: a Spanish corpus annotated with negation for sentiment analysis. A typology of negation patterns

In this paper, we present SFU ReviewSP-NEG, the first Spanish corpus annotated with negation with a wide coverage freely available. We describe the methodology applied in the annotation of the corpus including the tagset, the linguistic criteria and the inter-annotator agreement tests. We also include a complete typology of negation patterns in Spanish. This typology has the advantage that it is easy to express in terms of a tagset for corpus annotation: the types are clearly defined, which avoids ambiguity in the annotation process, and they provide wide coverage (i.e. they resolved all the cases occurring in the corpus). We use the SFU ReviewSP as a base in order to make the annotations. The corpus consists of 400 reviews, 221,866 words and 9455 sentences, out of which 3022 sentences contain at least one negation structure.