Negators, modals, and degree adverbs can significantly affect the sentiment of the words they modify. Often, their impact is modeled with simple heuristics; although, recent work has shown that such heuristics do not capture the true sentiment of multi-word phrases. We created a dataset of phrases that include various negators, modals, and degree adverbs, as well as their combinations. Both the phrases and their constituent content words were annotated with real-valued scores of sentiment association. Using phrasal terms in the created dataset, we analyze the impact of individual modifiers and the average effect of the groups of modifiers on overall sentiment. We find that the effect of modifiers varies substantially among the members of the same group. Furthermore, each individual modifier can affect sentiment words in different ways. Therefore, solutions based on statistical learning seem more promising than fixed hand-crafted rules on the task of automatic sentiment prediction.
Contextual Valence Shifters: Negators, modals, and degree adverbs impact the sentiment of the word or phrase they modify and are commonly referred to as contextual valence shifters @cite_30 @cite_32 @cite_1 @cite_19 @cite_24 . Conventionally, the impact of contextual valence shifters is captured by simple heuristics. For example, negation is often handled by reversing the polarities of the sentiment words in the scope of negation @cite_30 @cite_32 @cite_5 or by shifting the sentiment score of a term in a negated context towards the opposite polarity by a fixed amount @cite_28 . However, such heuristics do not adequately capture the true sentiment of multi-word expressions @cite_17 . relax the assumption of a fixed shifting margin and estimate these margins for each modifier separately from data. , on the other hand, estimate the impact of negation on each individual sentiment word through a corpus-based statistical method. automatically rank English adverbs by their intensifying or diminishing effect on adjectives using ratings metadata from product reviews.
@cite_30 @cite_28 @cite_1 @cite_32 @cite_24 @cite_19 @cite_5 @cite_17
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In addition to describing facts and events, texts often communicate information about the attitude of the writer or various participants towards material being described. The most salient clues about attitude are provided by the lexical choice of the writer but, as discussed below, the organization of the text also contributes information relevant to assessing attitude. We argue that the current work in this area that concentrates mainly on the negative or positive attitude communicated by individual terms (Edmonds and Hirst, 2002; Hatzivassiloglou and McKeown, 1997; Turney and Littman, 2002; , 2001) is incomplete and often gives the wrong results when implemented directly. We then describe how the base attitudinal valence of a lexical item is modified by lexical and discourse context and propose a simple, “proof of concept” implementation for some contextual shifters. We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability. 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. Proper treatment of negation is an important characteristic of methods for sentiment analysis. However, while there is a growing body of research on the automatic resolution of negation, it is not yet clear as to how negation is best represented for different applications. To begin to address this issue, we review representation alternatives and present a state-of-the-art system for negation resolution that is interoperable across these schemes. By employing different configurations of this system as a component in a test bed for lexically-based sentiment classification, we demonstrate that the choice of representation can have a significant impact on downstream processing. 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. Determining the polarity of a sentiment-bearing expression requires more than a simple bag-of-words approach. In particular, words or constituents within the expression can interact with each other to yield a particular overall polarity. In this paper, we view such subsentential interactions in light of compositional semantics, and present a novel learning-based approach that incorporates structural inference motivated by compositional semantics into the learning procedure. Our experiments show that (1) simple heuristics based on compositional semantics can perform better than learning-based methods that do not incorporate compositional semantics (accuracy of 89.7 vs. 89.1 ), but (2) a method that integrates compositional semantics into learning performs better than all other alternatives (90.7 ). We also find that "content-word negators", not widely employed in previous work, play an important role in determining expression-level polarity. Finally, in contrast to conventional wisdom, we find that expression-level classification accuracy uniformly decreases as additional, potentially disambiguating, context is considered. 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.