Blog 19: Comparative Sentiment Analysis

9 mars 2022


Research Notebook

'Mixology' is an open research project, which aims to extract opinions in times of crisis, here from a corpus collected via the Twitter API, from December 12 to 31, 2021.

As already noted (see Blog 10, Blog 12 and Blog 13), the sentiment analysis results based on a lexicon approach can vary considerably depending on the lexicon used. In this research, two observations were confirmed: the more a dictionary is in adequacy with the domain, the more the results will be precise. Let us add that the number of terms included in the lexicon is also as significant because the language is not limited to a handful of words (that’s its richness too). In the comparative sentiment analysis for the entire « sanitary measures » sub-corpus (in English), the observations cannot lead to the assertion that one type of sentiment prevails more widely than another, regardless of the lexicon used. If we stick to the average scores obtained by sentiment type, the negative one totals 37.43%, against 44.75% for the positive one. This lack of significant difference confirms a trend towards the polarization of debates already observed concerning the Covid crisis, whether approached from a political or health angle. However, this must be refined according to the country of origin of the tweets but also to the topic addressed.


Below is the R function used to perform a comparative sentiment analysis, which includes seven English-language lexicons, including the two lexicons (Mixology Lexicon and Mixology Covid Lexicon) developed as part of this research (see Blog 14).


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Read more

Blog 21: Politicians, experts, and journalists

Blog 20: For vaccination, against restrictions

Blog 19: Comparative Sentiment Analysis

Blog18: A health and political crisis

Blog 17: Anatomy of the “political/sanitary measures” sub-corpus (en)

Blog 16: Sentiment analysis of the ‘vaccination’ sub-corpus (en, part.2)

Blog 15: Comparative sentiment analysis of the ‘vaccination’ sub-corpus (en, part.1)

Blog 14: An adapted dictionary for the Covid crisis and sentiment analysis

Blog 13: Building a stop words list

Blog 12: Main Dictionaries for Sentiment Analysis

Blog 11: Statistical description of the corpus #RStats

Blog 10: Sentiment analysis or the assessment of subjectivity

Blog 9: Topic modeling of the ‘vaccination’ corpus (English)

Blog 8: Linguistic and quantitative processing of the ‘vaccination’ corpus (English, part.2)

Blog 7: Linguistic and quantitative processing of the ‘vaccination’ corpus (English, part.1)

Blog 6: Collecting the corpus and preparing the lexical analysis

Blog 5: The textclean package

Blog 4: Refining the queries

Blog 3: The rtweet package

Blog 2: Collecting the corpus

Blog 1: An open research project

The challenges of research on media use in times of crisis