Opinion mining is another name for sentiment analysis. It is a natural language processing (NLP) that focuses on determining the sentiment or emotion expressed in a piece of tech. The goal is to unravel the subjective idea in the text and then categorize it into different class sentiments. For example, sentiment categorizations are often themed as positive, negative, or neutral. By sentiment analysis, what goes on consistently in the background of the mind of a statement author could identified.
The general objective of this project is to understand what takes centre stage in the minds of Americans, particularly the people of the US and Canada. The importance of this is that it produces understanding and insights into how people's, for example, customer's, emotions could be managed to the organization's advantage using tools such as social media campaigns and reputation management.
The dataset for this analysis is composed of sentiment, and text data, comprised of different countries, from the year 2014 to 2013.
The original data was sourced from, see here. The dataset records emotions, trends, and interactions across various social media platforms. The dataset is user-generated; it provides a snapshot of user-generated content, encompassing text, timestamps, hashtags, countries, likes, and retweets. Each entry presents unique stories—moments of surprise, excitement, admiration, thrill, contentment, and more—shared by individuals worldwide.
The original dataset was filtered to focus on the US and Canada-specific data.
A sub-dataset comprising US and Canada-only sentiment data was created out of the broad data set. See it here.. The following Python code with dedicated libraries was employed to perform the filtering.
This analysis buttresses the fact that the preoccupation of the US for the period under analysis was 'new'. It appeared that Americans were more concerned about how to move to achieve a new state of phase. That is, between 2014 and 2023, America was concerned more about achieving the 'next stage of things'. It could underscore the desire to get new material things such as 'new houses', and 'new cars'. It could also indicate a new state of personal affairs such as a 'new job',' new wife', etc. While Canada during the period of review cared more about their lives and survival. This could imply a parallel between the Canadian healthcare system and the people
- NLP( Natural Language Processing) libraries of Python were mainly employed to work out this analysis The general codes for the analysis iterations could be found here.
- Credits: data