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You can use the download statistical inference in blend to your incredible unpredictability. We now compare the results of our statistical inferences to the manually selected dictionaries from previous research. For this purpose, Table 4 details the number of overlapping terms and compares to what extent classifications agree. In addition, we present the inter-rater reliability i. Here, a reliability value of 1 indicates a perfect overlap between the classifications in positive and negative groups, whereas a value of 0 denotes that human dictionaries and our statistical inferences are statistically unrelated.
The results demonstrate that the ex ante selected dictionaries show only a small overlap with the word lists from our statistical procedure. In the case of movie reviews, only out of i. Out of these, only This is in line with our in-depth investigations, since many negative expressions from this dictionary feature a positive connotation in the context of movie evaluations.
Psychological dictionaries classify words, such as such as crime , force or war , in the negative list, while, in film reviews, these often refer in a positive sense to the suspense in certain scenes. Unsurprisingly, we find the highest number of overlapping terms in the dictionary that includes the most entries, i. However, this dictionary shows the lowest reliability 0.
In contrast, the highest reliability 0. We observe similar results for our financial disclosures, where 55 out of extracted words i. Out of these, Overall, we find a correlation of 0. Even the dictionaries that were specifically designed for financial reports reveal large deviations from the statistical inferences. We observe only a total number of 21 overlapping terms for the Henry dictionary, and 20 for the Loughran-McDonald dictionary.
Nonetheless, compared to psychological dictionaries, we see that the finance-specific dictionaries are indeed more accurate in measuring the reception of words in financial disclosures. For example, the Loughran-McDonald dictionary shows a consensus classification of Moreover, finance-specific dictionaries also yield the highest reliability. Table 4 identifies a consistent disagreement between human classification and statistical selection. Although most ex ante dictionaries feature a large volume of words, many statistically relevant terms are not included.
As a consequence, misclassification and the erroneous exclusion of words limit the suitability of ex ante dictionaries. The aforementioned dictionaries have frequently been utilized also in predictive settings and we thus also compare the out-of-sample performance of the above dictionaries with our method. We briefly outline the results here, while we provide further statistics and elaboration in our supplementary materials. In short, our method outperforms all of the investigated dictionaries for both movie reviews and financial disclosures.
In the case of movie reviews, the best performing dictionary Harvard IV results in a We observe a similar pattern for financial disclosures. These results thus reinforce our previous finding that manually selected dictionaries deviate from true perception. Human-generated dictionaries commonly categorize only isolated words without incorporating any contextual information.
However, the position of a word in a sentence is likely to contribute to the meaning and the overall interpretation.
Statistical inferences for polarity identification in natural language
Consequently, related research attempts to work with higher-order word combinations, i. However, findings indicate mixed results regarding the extent to which their inclusion improves performance. Expert dictionaries refrain from labeling word pairs, since it requires considerable manual labor.
Similarly, heuristics for dictionary creation are also rarely designed to process n -grams. This is in contrast to our statistical procedure, which works effortlessly with n -grams as the corresponding frequencies are simply inserted in the variable selection procedure. These benefits become particularly evident when considering the sheer number of input variables bigrams for financial filings and bigrams for movie reviews.
Such large numbers of highly correlated predictors would imply serious overfitting issues for almost any type of statistical model without variable selection. Table 5 compares the results from using n -grams. First of all, we observe fewer relevant bigrams than unigrams. In the case of unigrams, our method extracts relevant terms from the movie reviews and from the financial corpus, while using bigrams results in a total number of terms for movie reviews and 51 for financial filings.
We provide the complete lists of extracted phrases in the supplementary materials due to space limitations, but summarize a few intriguing insights here. For instance, the bigram with the highest positive coefficient in the review corpus is best film , while the most negative bigrams are bad movie and waste time. According to Table 5 , we also observe a drop in the adjusted R 2 for both corpora. In the case of movie reviews, the adjusted R 2 declines from 0.
We observe a similar pattern for our financial corpus. Here, the adjusted R 2 decreases from 0. Finally, we also tested a configuration that incorporates both unigrams and bigrams. While this approach yields the highest fit for the review corpus, we observe a slightly inferior goodness-of-fit for the financial corpus. Altogether, this shows that our method is not limited to single terms, but also serves as an appropriate tool to study the influence of higher-order word combinations, and even phrases, on a response variable.
Our method presents also a valuable tool for analyzing behavioral research questions.
This section demonstrates two applications that allow for the testing of hypotheses with focus on word choice. We utilize our method to test where authors place negative statements in their reviews. Writers might start with negative thoughts, as suggested by the law of primacy in persuasion. On the other hand, one might be inclined to instead utilize the regency effect, according to which arguments presented last garner more attention. Given the overall movie rating, we can evaluate where authors place negative information when composing movie reviews, i.
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H ypothesis : Negative information is more likely to be placed at the end than at the beginning of a review. In order to test this hypothesis, we compute the sentiment of the first and second half of each review by summing over products of coefficient and weighted term frequency. In addition, we present the same statistics for reviews that are filtered for a positive Panel II or negative Panel III gold standard only.
We then test the null hypotheses respectively. According to our results, the second half of movie reviews generally conveys a more negative tone than the first half. This difference is also significant at the 0. In Panel II, we observe a similar pattern for reviews with positive ratings t -value of 6. We thus accept our hypothesis regarding the presence of a regency effect.
This result also coincides with psychological research according to which senders of information are more likely to place negative content at the end [ 44 ], but, in contrast, our evidence is collected outside of an artificial laboratory setting, as it stemms from actual human communication.
In our second application of hypothesis testing, we examine to what extent financial markets trade upon non-informative wording. Previous works have established a robust market response to fact-related information encoded in written materials, which is primarily measured by using the positive and negative word lists from Loughran-McDonald or Harvard IV. Yet it is unclear how the remaining words—which are not deemed as either positive or negative from a external standpoint and which we refer to as non-informative—are processed by markets.
Consistent with classical economic theory, we expect that investors ignore these terms and, instead, solely focus on essential, fact-related information, i. H ypothesis : Financial markets are not distracted by the wording in corporate communication that falls outside the clearly delineated categories of positive and negative. Interestingly, we present empirical results in the following section which reject the above hypothesis and suggest the opposite. The extracted words from Table 3 list the polarity terms that are statistically relevant for the investment decisions of traders.
However, most of them are not necessarily classified as positive or negative according to the Harvard IV psychological or Loughran-McDonald finance-specific dictionary. We thus test our hypothesis by grouping all words into two categories according to the previous dictionaries: one group contains all words that are labeled as either positive or negative. This group represents all terms that feature an explicit, fact-based statement. The remaining entries form a group that can be characterized as non-informative wording. For instance, the latter contains entries such as although and however.
We find that the perception of investors depends on many terms that feature no explicit positive or negative statement polarity. According to the Harvard IV dictionary, only The Loughran-McDonald dictionary presents a similar picture. Here, the fact-based group contains Finally, we perform an F -test to validate whether the subset of words that are neither labeled as positive nor negative has a combined effect on stock returns. In the case of the Harvard IV dictionary, this results in an F -statistic of 5. Similarly, the F -statistic for the Loughran-McDonald dictionary numbers to 5.
We must thus reject our hypothesis and provide evidence that expressions deemed as non-informative wording by previous research have a statistically significant effect on financial markets. In the following, we discuss the implications of our research method as it not only improves understanding of natural language but also enables intriguing inferences in behavioral sciences. Furthermore, our research is highly relevant for practitioners seeking to operationalize natural language in Information Systems.
Understanding decision-making and providing decision support both increasingly rely upon computerized natural language processing. In contrast to many black-box methods from the domain of machine learning, our methodology provides a vehicle for content analysis and opinion mining that is fully comprehensible for deep insights.
Specifically, it allows one to maintain high interpretability as it explains an effect in terms of the presence of individual words. It thus allows researchers to dissect the relationship between natural language and a given outcome variable. In addition, our approach goes beyond pre-defined dictionaries that classify words into groups of positive and negative words as we assign individual word weights to each word, thereby accounting for differences in the valence levels of words of the same polarity class.
Our results indicate that common, manually selected dictionaries from the literature, such as the Harvard IV psychological dictionary, are neither complete nor adequate for arbitrary domains. For instance, in the area of finance, they classify words as positive that are not necessarily interpreted positively by investors. To overcome these previous limitations, our methodology provides a means by which to automate the process of dictionary generation.
Altogether, our study thus provides evidence that applications of dictionary-based sentiment analysis can be significantly improved when adapting the dictionaries to the corresponding domain. Analyzing the perceptions of word choice and understanding the response to natural language on a granular level can yield new insights in a large number of use cases. In the following points, we illustrate prominent applications in the areas of both practice and research:.
These examples highlight several prominent applications that benefit from a granular understanding of language at word level. Ultimately, it is hoped that the contributions and advantages presented in this paper—such as quantifying the reception of language—will become an important tool in future research papers. Application of this method can yield novel insights into behavioral research questions regarding the information processing of natural language.
This should help those in the field of social sciences to add to the growing body of knowledge on the role of behavior in individual decisions and population-wide outcomes, such as voting, consumer demand, information sharing, product evaluation and opinion aggregation. As demonstrated in this paper, our methodology has the potential to enable unprecedented opportunities in terms of validating behavioral research outside of existing laboratory setups. Yet it also fuels innovations in the theoretical advancement and formalization of theories as its high interpretive power facilitates new discoveries.
Our approach gauges the domain-specific effects of words or n -grams based on a decision variable. A sufficient number of labeled training data is thus a necessary prerequisite to extract statistically relevant terms from documents. However, at the same time, this presents also the strength of our approach as we explain the variation in the dependent variable through word use. This work is targeted at the vast number of users of dictionary-based approaches.
The objective behind dictionaries is that they obtain polarity scores that are context-independent. These are known issues in computer science, since an exact understanding of language remains a daunting undertaking. As a remedy, we suggest the following extension to our framework: if desired, one could extend the LASSO-based approach with a hierarchical formulation, such that terms are associated with context-specific polarity score; however, the resulting caveats are a larger corpora, the challenges from a context-dependent interpretation and the mismatch with the majority of dictionary-based use cases.
This framework comes with the following limitations, for which we also name straightforward remedies. One the one hand, dependent variables with other distributional assumptions can be easily handled by fitting the framework with a fully Bayesian approach that relies upon Markov chain Monte Carlo MCMC sampling. On the other hand, the Post-LASSO makes mathematical assumptions regarding the derivation of the confidence intervals. As an alternative, one can either revert to the significance test from [ 38 ] or again utilize a MCMC-based estimation.
The latter is especially prone to co-occurrence patterns of words when computing confidence intervals. Notably, the two aforementioned adaptations do not change the overall model specification but merely exchange the underlying estimation technique. Understanding the decision-making of individuals, enterprises and organizations presents a fundamental pillar of behavioral research. However, the challenges associated with processing natural language have been largely associated with simple decision models featuring predominantly structured data. Yet an unparalleled source of information is encoded in unstructured formats, and especially textual materials.
The reasons behind this are multifaceted, including the recent advent of the big data era and the increasing availability of data through the World Wide Web, which has made a vast number of written documents—such as user-generated content and news—available to the public. Past research has laid the groundwork for inferring the polarity of written contents, albeit in a manner that is usually limited to a few psychological dictionaries that classify single terms. Such approaches work almost out-of-the-box and thus seem promising at first, but entail inevitable and major shortcomings.
The elements of these word lists are selected ex ante by manual inspection and subjective judgment. As such, our paper exposes the weaknesses of common dictionary methods: they only allow one to assess the overall polarity of documents and not of individual expressions, thereby leaving any deeper insights in the underlying text processing untapped. In addition, they often prove insufficient in adequately reflecting the domain-specific perception of a given audience. As a remedy, this paper proposes the use of LASSO regularization as a form of variable selection to extract relevant words that statistically impact decisions.
Social science researchers can greatly benefit from such a procedure, as it infers ex post relevant terms based on the outcome of a decision. It can therefore efficiently adapt to domain-specific peculiarities of narratives and discriminate between subtle polarity levels across words. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.
Abstract Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. Funding: The authors received no specific funding for this work. Introduction The power of word choice and linguistic style is undisputed in the social sciences. Backgrounds This section posits that extracting statistically relevant terms based on a decision variable is both an innovative and relevant research question to the social sciences. Relationship to opinion mining Drawing inferences regarding how wording relates to a decision variable is closely related to the concept known as sentiment analysis or opinion mining.
Overview of common dictionaries Gaining insights into the subtle differences between word choice requires methods that analyze narrative content at a granular level. Download: PPT. Statistical approaches for dictionary generation The objective of this work is to come up with a statistical procedure that deduces the true perception of explicit and implicit polarity terms. Research gap Altogether, we see that the above research neglects to draw rigorous statistical inferences from a comparison between word choice and the regressands.
Method development This section proposes a novel methodology by which to investigate the granular perception of natural language and to examine the textual cues that trigger decision-making. Preprocessing of natural language The preprocessing phase transforms the running text into a structured format that allows for further calculations. Model specification Let y denote the gold standard that measures our response variable of interest. Empirical results This section evaluates our method with two studies from different domains: I we investigate the role of word choice in recommender systems by extracting opinionated terms from user-generated reviews.
Study I: Opinionated terms in user-generated reviews Corpus with reviews. Statistical inferences for polarity word scoring. Table 2. Empirical results of top 15 opinionated terms in movie reviews. Study II: Impact of wording on financial markets Financial corpus. Statistical inferences for word reception. Table 3. Empirical results of top 15 polarity expressions in financial filings. Comparison to dictionaries from human selection We now compare the results of our statistical inferences to the manually selected dictionaries from previous research.