This paper details LTG-Oslo team's participation in the sentiment track of the NEGES 2019 evaluation campaign. We participated in the task with a hierarchical multi-task network, which used shared lower-layers in a deep BiLSTM to predict negation, while the higher layers were dedicated to predicting document-level sentiment. The multi-task component shows promise as a way to incorporate information on negation into deep neural sentiment classifiers, despite the fact that the absolute results on the test set were relatively low for a binary classification task.
Negation is a pervasive linguistic phenomenon which has a direct effect on the sentiment of a text @cite_29 . Take the following example from the SFU ReviewSP-Neg training data, where the negation cue is shown in and the scope is underlined .
@cite_29
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Negation words, such as no and not, play a fundamental role in modifying sentiment of textual expressions. We will refer to a negation word as the negator and the text span within the scope of the negator as the argument. Commonly used heuristics to estimate the sentiment of negated expressions rely simply on the sentiment of argument (and not on the negator or the argument itself). We use a sentiment treebank to show that these existing heuristics are poor estimators of sentiment. We then modify these heuristics to be dependent on the negators and show that this improves prediction. Next, we evaluate a recently proposed composition model (, 2013) that relies on both the negator and the argument. This model learns the syntax and semantics of the negator’s argument with a recursive neural network. We show that this approach performs better than those mentioned above. In addition, we explicitly incorporate the prior sentiment of the argument and observe that this information can help reduce fitting errors. This paper presents a survey on the role of negation in sentiment analysis. Negation is a very common linguistic construction that affects polarity and, therefore, needs to be taken into consideration in sentiment analysis. We will present various computational approaches modeling negation in sentiment analysis. We will, in particular, focus on aspects, such as level of representation used for sentiment analysis, negation word detection and scope of negation. We will also discuss limits and challenges of negation modeling on that task. We investigate the problem of determining the polarity of sentiments when one or more occurrences of a negation term such as "not" appear in a sentence. The concept of the scope of a negation term is introduced. By using a parse tree and typed dependencies generated by a parser and special rules proposed by us, we provide a procedure to identify the scope of each negation term. Experimental results show that the identification of the scope of negation improves both the accuracy of sentiment analysis and the retrieval effectiveness of opinion retrieval. Automatic detection of linguistic negation in free text is a critical need for many text processing applications, including sentiment analysis. This paper presents a negation detection system based on a conditional random field modeled using features from an English dependency parser. The scope of negation detection is limited to explicit rather than implied negations within single sentences. A new negation corpus is presented that was constructed for the domain of English product reviews obtained from the open web, and the proposed negation extraction system is evaluated against the reviews corpus as well as the standard BioScope negation corpus, achieving 80.0 and 75.5 F1 scores, respectively. The impact of accurate negation detection on a state-of-the-art sentiment analysis system is also reported.