What is Sentiment Analysis?

Now that we have completed all the key preprocessing steps and our example dataset is in much better shape, we can finally proceed with sentiment analysis.

What is Sentiment Analysis?

As social beings, our beliefs, understanding of reality, and everyday decisions are deeply shaped by the opinions, perceptions and evaluations of others. This social conditioning is a well-documented phenomenon in fields such as psychology, sociology, and communication, where it is understood that individuals often rely on external cues, especially the attitudes and judgments of others when forming their own assessments.

Understanding how public reaction and sentiment shapes and reflects collective perception has become central not only to corporate strategy, but also to scientific inquiry across many academic disciplines.

While the analysis of public opinion predates the Internet, the modern field of sentiment analysis did not gain momentum until the mid-2000s. This surge was largely driven by the rise of Web 2.0, which leveraged the internet into a more participatory platform, enabling users to create, share, and comment on content across chats, blogs, forums, and other social media. These digital spaces dramatically expanded the circulation and accessibility of user-generated content, creating a fertile ground for computational approaches to analyze subjective expressions in large volumes of text. But what is sentiment analysis?

Sentiment analysis, also known as opinion mining, is now a well-established area of study within natural language processing (NLP) and computational linguistics. It focuses on identifying and extracting people’s opinions, evaluations, attitudes, and emotions from written language.

Whether through product reviews, political commentary, or social media posts in virtually any possible topic of interest, sentiment analysis aims to quantify and interpret subjective information at scale, enabling applications in marketing, social science, finance, and beyond. In this course, we will explore ways of extracting insights from textual data, in particular how we can detect underlying emotions within messages shared by people on a popular streaming TV series.

Our analysis pipeline will follow a two-step approach. First, we will compute basic sentiment polarity to determine whether viewers who commented on both season finales reacted more negatively, neutrally, or positively. Next, we will apply a more fine-grained emotion detection technique to capture and analyze the specific emotional expressions conveyed in the text.

Let’s start by installing and loading the necessary packages, then bringing in the cleaned dataset so we can begin our sentiment analysis. We will discuss the role of each package in the next episodes.

# Install packages (remove comments for packages you might have skipped in previous episodes)
install.packages("sentimentr")
install.packages("syuzhet")
# install.packages("dplyr")
# install.packages("tidyr")
# install.packages("readr")
# install.packages("ggplot2")
# install.packages("RColorBrewer")
# install.packages("stringr")

# Load all packages
library(sentimentr)
library(syuzhet)
library(dplyr)
library(tidyr)
library(readr)
library(ggplot2)
library(RColorBrewer)
library(stringr)

# Load Data
comments <- readr::read_csv("./data/clean/comments_preprocessed.csv")

--- title: "What is Sentiment Analysis?" --- Now that we have completed all the key preprocessing steps and our example dataset is in much better shape, we can finally proceed with sentiment analysis. ![Image from Canva](images/sentiment.png){fig-align="center" width="500"} ## What is Sentiment Analysis? As social beings, our beliefs, understanding of reality, and everyday decisions are deeply shaped by the opinions, perceptions and evaluations of others. This social conditioning is a well-documented phenomenon in fields such as psychology, sociology, and communication, where it is understood that individuals often rely on external cues, especially the attitudes and judgments of others when forming their own assessments. Understanding how public reaction and sentiment shapes and reflects collective perception has become central not only to corporate strategy, but also to scientific inquiry across many academic disciplines. While the analysis of public opinion predates the Internet, the modern field of sentiment analysis did not gain momentum until the mid-2000s. This surge was largely driven by the rise of Web 2.0, which leveraged the internet into a more participatory platform, enabling users to create, share, and comment on content across chats, blogs, forums, and other social media. These digital spaces dramatically expanded the circulation and accessibility of user-generated content, creating a fertile ground for computational approaches to analyze subjective expressions in large volumes of text. But what is sentiment analysis? > *Sentiment analysis, also known as opinion mining, is now a well-established area of study within natural language processing (NLP) and computational linguistics. It focuses on identifying and extracting people’s opinions, evaluations, attitudes, and emotions from written language.* Whether through product reviews, political commentary, or social media posts in virtually any possible topic of interest, sentiment analysis aims to quantify and interpret subjective information at scale, enabling applications in marketing, social science, finance, and beyond. In this course, we will explore ways of extracting insights from textual data, in particular how we can detect underlying emotions within messages shared by people on a popular streaming TV series. Our analysis pipeline will follow a two-step approach. First, we will compute basic sentiment polarity to determine whether viewers who commented on both season finales reacted more negatively, neutrally, or positively. Next, we will apply a more fine-grained emotion detection technique to capture and analyze the specific emotional expressions conveyed in the text. Let’s start by installing and loading the necessary packages, then bringing in the cleaned dataset so we can begin our sentiment analysis. We will discuss the role of each package in the next episodes. ``` r # Install packages (remove comments for packages you might have skipped in previous episodes) install.packages("sentimentr") install.packages("syuzhet") # install.packages("dplyr") # install.packages("tidyr") # install.packages("readr") # install.packages("ggplot2") # install.packages("RColorBrewer") # install.packages("stringr") # Load all packages library(sentimentr) library(syuzhet) library(dplyr) library(tidyr) library(readr) library(ggplot2) library(RColorBrewer) library(stringr) # Load Data comments <- readr::read_csv("./data/clean/comments_preprocessed.csv") ```