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Research Topic Description, including Problem Statement:
Sentiment analysis is a field of Natural Language Processing (NLP) that deals with automating methods to quantitatively measure affect from unstructured textual data, usually surrounding a particular subject. Current methods of sentiment analysis apply numerical values to words and phrases based on a defined lexicon, or a supervised learning algorithm, and report metrics as an average over the queried string. Many currently-available models measure both the subjectivity (subjective verses objective statement) and the polarity (positive verses negative opinion) of a word or phrase. While modern methods of sentiment analysis are useful within the private sector for brand management and advertising, there are several limitations. For example, lexical disambiguation is difficult to quantify and relies on contextual clues to truly understand the sentiment of a phrase. For instance, the word “kill” can have a different polarity depending on context. Consider the sentences “Your customer service is killing me” and “You’re killing it!” In the first sentence, “kill” has an obvious negative polarity, while in the second sentence, its polarity is clearly positive. Developing comprehensive analytical models that account for lexical ambiguity remains a challenge. Culture presents another challenge to current methods of sentiment analysis. Communication styles (particularly when sharing opinions on the Internet) vary greatly across cultures, demographics, and the selected communication medium; and sentiment analysis tools are largely modeled after Western communication styles and behavior, and focus on social media. This limits the broad utility of currently-available techniques. Additionally, subjectivity and polarity may not be as relevant to non-Western cultural communication styles, thus other metrics should be evaluated. By developing more comprehensive machine learning algorithms, one can expand the applicability of sentiment analysis. Identifying parameters beyond polarity and subjectivity, which are applicable to both Western and non-Western communications styles, would enable the development of more robust sentiment analyzers with broader applicability.
Example Approaches:
There are different ways that this research can be approached. This topic broadly encompasses the fields of data science, mathematics, sociology, and linguistics; thus collaborative and versatile approaches are encouraged. Researchers could focus on developing a more robust classification algorithm for sentiment analysis that accounts for cultural online communication differences and contextual clues in addition to lexical polarity.
Proposals could consider one or more of the following:
Relevance to the Intelligence Community:
Robust sentiment analyzers for textual data acquired from internet sources, such as news and social media, can be used to gain insight into the hearts and minds of any group of people.This could include how the group perceives a government policy, or their perception of U.S. operations and policies. A comprehensive method for analyzing sentiment surrounding U.S. policy in target populations that is culturally-agnostic is imperative for policy makers to make informed decisions. Thus, research in this area will enable significant secondary effects in future intelligence operations.
Key Words: Natural Language Processing, Sentiment Analysis, Cultural Differences, Social Media, Machine Learning, Model, NLP, Affect, Emotion, Sentiment, Text Classification, Culturally Agnostic
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