On Positivity Bias in Negative Reviews (2021)

Abstract: Prior work has revealed that positive words occur more frequently than negative words in human expressions, which is typically attributed to positivity bias, a tendency for people to report positive views of reality. But what about the language used in negative reviews? Consistent with prior work, we show that English negative reviews tend to contain more positive words than negative words, using a variety of datasets. We reconcile this observation with prior findings on the pragmatics of negation, and show that negations are commonly associated with positive words in negative reviews. Furthermore, in negative reviews, the majority of sentences with positive words express negative opinions based on sentiment classifiers, indicating some form of negation.

## introduction a battery of studies have validated the pollyanna hypothesis that positive words occur more frequently than negative words in human expressions, using corpora ranging from google books to @xcite @xcite . the typical interpretation is connected with the positivity bias, which broadly denotes 1) a tendency for people to report positive views of reality, 2) a tendency to hold positive expectations, views, and memories, and 3) a tendency to favor positive information in reasoning @xcite @xcite . however, it remains an open question whether the pollyanna hypothesis holds in negative reviews, where the communicative goal is to express negative opinions. in this work, we use a wide variety of review datasets to examine the use of positive and negative words in negative reviews. the excitement to try the place and deemed the food ok. zooming into individual words, they used the same number of positive and negative words in this negative review. interestingly, this short review has as many as three negations, one directly applied to "great" (hence "not great"). more generally, we find that negative reviews contain more positive words than negative words, which is consistent with the pollyanna hypothesis. two possible reasons may explain this observation: 1) negative reviews tend to still include positive opinions due to a naïve interpretation of the positivity bias, where positive words express positive sentiments without accounting for negation or other contextual meaning of these words; 2) negative reviews tend to use indirect expressions (i.e., applying negations to positive words) to indicate negative opinions (e.g., "not clean"). note that a broad interpretation of positivity bias may encompass the second reason, foot_0 but indirect expressions could also be related to other factors, e.g., verbal politeness @xcite ). we aim to delineate these two reasons by examining the use of negations. our results provide support for the latter reason: negative reviews tend to use more negations than positive reviews. the differences become even more salient when we compare negations applied to positive words vs. negative words. finally, among sentences with positive words in negative reviews, the majority are classified as negative than as positive by sentiment classifiers, indicating some form of negation. ## related work in addition to positivity bias, our work is closely related to experimental studies on understanding the effect of direct (e.g., "bad") and indirect (e.g., "not good") wordings. colston (1999) and @xcite observe no difference in people's interpretation of direct and indirect wordings in negative opinions; but direct wordings receive higher evaluations than indirect ones in positive opinions. in this work, we examine whether and how individuals use indirect wordings in practice (in negative reviews). our work is also related to @xcite , which finds that negation is used more frequently in negative reviews and is thus pragmatically negative. we extend @xcite in two ways: 1) we demonstrate a high frequency of negation followed by positive words in negative reviews compared to other combinations, a new observation motivated through the lens of positivity bias; 2) we conduct a systematic study using a wide variety of datasets with multiple dictionaries. finally, our work builds on sentiment classification @xcite @xcite . the nlp community has made significant progress in recognizing the sentiment in texts of various languages, obtaining accuracies of over 95% (english) in binary classification @xcite . researchers have also developed novel approaches to identify fine-grained sentiments (e.g., aspect-level sentiment analysis @xcite @xcite ) as well as semi-supervised and unsupervised approaches @xcite @xcite . ## datasets we use a wide range of english review datasets to ensure that our results are robust across domains. we use spacy to tokenize the reviews in all datasets @xcite , except that stanford core nlp is used to tokenize sst reviews @xcite . we present results for amazon reviews in the appendix, and our main results are robust on amazon reviews. our code is available at https://github.com/madhu-aithal/positivitybias-in-negative-reviews . length of positive vs. negative opinions. in general, negative opinions tend to be longer than positive opinions (@xmath0.05 after bonferroni correction in 6 out 10 datasets; see the appendix for details). in comparison, the difference in length is smaller at the sentence level (figure 1 ). therefore, we use sentences as the basic unit in this work. to further rule out sentence length as a confounding factor, we also present word-level results in the appendix. ## results we first investigate the occurrences of positive words, negative words, and negations in reviews. we find that negative reviews contain more positive words than negative words in all datasets. we show that this observation relates to the prevalence of negation in negative reviews compared to positive reviews in all datasets. furthermore, these negations are commonly associated with positive words in all datasets, and sentences with positive words tend to be negative based on sentence-level prediction, supporting the prevalence of indirect wordings in negative reviews. ## conclusion in this paper, we investigate positivity bias in negative reviews and highlight the role of indirect expressions in understanding this phenomenon. we show that negations followed by positive words are more prevalent than any other combination in negative reviews. given that these indirect wordings account for only 11.3% of the occurrences of positive words in negative reviews, we further show that such sentences with positive words tend to be negative, based on sentiment classifiers. while our findings support the prevalence of indirect expressions, we do not take sentiment intensity into account. in practice, "not good" provides a different meaning from "not amazing". we believe exploring the relationship between negation and semantic intensity is a promising direction. our lexical-driven approaches are limited by the lexicons included in the dictionaries, which are typically evaluated independent of the context, so their sentiment may be different in the specific context. 7 similarly, our sentence-level prediction results are limited by the distribution shift when applying bert trained on documents to sentences. it is reassuring that our high-level results hold across multiple datasets based on both lexical-driven approaches and sentence-level prediction. as our study focuses on negative reviews in english, it is important to examine the generalizability of our results. for instance, it is important to understand to what extent the observed positivity bias in general expressions is driven by such indirect expressions. another natural extension is to investigate other languages. although our findings are limited to english reviews, we believe that they may be applicable to negative opinions in other languages, as pollyanna hypothesis @xcite has been validated across languages and cultures. finally, our work has implications for sentiment-related applications in nlp. the prevalence of indirect expressions in negative reviews underscores the importance of modeling and understanding negation in sentiment analysis and sentiment transfer @xcite . in general, we believe that online reviews not only provide valuable data for teaching machines to recognize sentiments but also allow us to understand how humans express sentiments. we hope that our work encourages future work to further investigate the framing choices when we express emotions and opinions, and their implications on nlp applications.