A detailed guide to sentiment analysis in 2023

Sydney Vieira

A complete guide to TV sentiment analysis

Sentiment analysis is becoming increasingly popular and influential in the TV industry. By allowing media companies to gain insights into topics discussed during TV shows, movies and advertisements, sentiment analysis has become one of the most prominent rising media trends in 2023. 

This article looks at how sentiment analysis can help businesses with brand reputation management, contextual targeting for advertising, and boosting business performance and strategy. We also discuss the process behind sentiment analysis as well as the different types of analyses currently available.

What is Sentiment Analysis?

Sentiment analysis, also called "opinion mining”, is a process that uses computational methods to extrapolate the overall sentiment, emotions, and opinions expressed in television content. The goal of conducting a sentiment analysis is to determine whether the tone and attitude conveyed by certain TV content are negative, positive, or neutral. 

Sentiment analysis is mainly used to understand consumer satisfaction, social acceptance, brand reputation, and general public sentiment on a product or service by observing social or public data. This is done by turning spoken language on TV into text via transcription, which is then analysed by natural language processing (NLP) technology.

How Does it Work?

Sentiment analysis for television often involves many phases. First, spoken television content is converted into text by automated transcription technology. Next, stop words, punctuation, and special characters are removed from the text as part of the pre-processing procedure.

Second, a sentiment analysis method is applied to the text created. Typically, feature extraction and classification are the critical components involved. In the feature extraction phase, the algorithm finds textual characteristics or attributes indicative of sentiment. These characteristics are then used to classify the sentiment of the text into a number of predetermined categories, such as positive, negative, or neutral.

Sentiment analysis techniques

Using machine learning and natural language processing (NLP) algorithms, sentiment analysis can quickly and accurately determine the emotional undertone of conversations taking place on television.

You can use various methods in sentiment analysis models, depending on the amount of data you need to analyse and the degree of accuracy you need for your model.

The main approaches to sentiment analysis are

  • Rule-based: These systems use manually-predefined rules or patterns to identify sentiment.
  • Machine-learning(or automatic): These systems use algorithms that were trained on large datasets and use statistical methods to determine sentiment.
  • Hybrid: these systems combine rule-based and machine-learning approaches to identify sentiment

Types of sentiment analysis

Sentiment analysis focuses on a text's polarity, whether positive, negative, or neutral. However, it can also go beyond polarity to identify specific feelings and emotions, such as anger, happiness, sadness, urgency (urgent, not urgent), and intentions (interested vs uninterested).

Depending on how you want to interpret topics, you can specify and customise your categories to meet your sentiment analysis needs. Here are some examples of the most popular types of sentiment analysis.

Graded sentiment analysis

Adding more positive and negative grades to your polarity categories can help determine the nuances of television sentiment.

  • Very positive, positive, neutral, negative, and very negative

This technique is commonly referred to as "graded" or "fine-grained sentiment analysis," and it can be used to interpret more positive and less positive sentiment.

Entity-based sentiment analysis

We use entity-based sentiment analysis to identify which specific topics or entities are being discussed and whether the sentiment is positive, neutral, or negative.

For instance, if a show states, "These trousers are way too baggy," the entity being discussed is the trousers, as determined by the entity-based classifier. This way, we learn more details about the entity from identifying the polarity of the sentiment.

Multilingual sentiment analysis

Last but not least, multilingual sentiment analysis is the process of discovering sentiment in a body of multilingual text. Often, external language models are needed for this task because it can be pretty challenging. 

The majority of these tools—such as sentiment lexicons—are accessible online, while others—such as translated corpora or noise detection algorithms—require coding knowledge to use.

Alternatively, you may use a language classifier to automatically detect the language in texts, after which you train a unique sentiment analysis model to categorise texts according to the language of your choice.

Why is sentiment analysis important?

Sentiment analysis is quickly evolving into a crucial tool to monitor and comprehend sentiment in all types of data since people express their thoughts and emotions more freely than ever.

Brands can discover what makes customers happy or frustrated by automatically analysing customer feedback, including comments in survey responses, TV mentions, and social media discussions so that they can design goods and services suitable for their target market.

You can monitor sentiment to identify and address positive and negative mentions of brands, topics or product as soon as possible. To determine whether you need to take action, you can compare sentiment between channels or programmes. Moreover, you could delve even deeper into your qualitative data to discover the causes of sentiment changes.

In summary, applying sentiment analysis can benefit your business in several ways, from saving time and increasing productivity to digging deeper for meaningful insights. In the next section, we look at how sentiment analysis can be used and how it can benefit businesses.

The benefits of sentiment analysis for TV

Thanks to new technologies, such as sentiment analysis tools, companies can now distinguish and classify feelings toward their brands, goods, and services.

Here are some of the most valuable benefits provided by sentiment analysis:

  • It enables taking quick action against negative sentiment to turn it around
  • Understand which topics are associated positively or negatively with your brand or industry 
  • Discover trends in sentiment around cultural topics 
  • Boost business performance and strategy
  • Management and monitoring of your brand's reputation
  • Decrease customer churn by prioritising your customer’s feelings at all times

Public opinion monitoring

Sentiment analysis is critical for monitoring public opinion. It provides companies with insight into the feelings and ideas expressed by the public on TV, allowing them to make educated decisions, adapt to shifts in public opinion, and track the effects of events and campaigns in real-time. 

Informs contextual targeting

By enabling advertisers to gauge the tone of the content surrounding their ads, sentiment analysis can offer valuable data for contextual targeting. Businesses can use this information to assess the relevance and efficacy of advertisements and avoid inappropriate or sensitive circumstances, thus increasing the impact and performance of advertising campaigns.

Topic and trend analysis

Sentiment analysis of topics and trends on TV provides valuable insights into perceptions and opinions on various subjects. By analysing the sentiment expressed in TV content advertisers can get a better understanding of what influences an audience and make informed decisions. Sentiment analysis can provide a better understanding of the cultural and topical sentiments presented in TV content. This information can be used to identify emerging cultural trends and monitor changes in cultural or topical sentiments over time. 

Brand reputation management

By giving businesses insightful information about how the public perceives their brand, sentiment analysis plays a critical role in brand reputation management. This data may be used to identify and address unfavourable sentiment, keep track of the results of marketing and communication initiatives, and anticipate market trends, ultimately assisting businesses in safeguarding and enhancing their brand reputation.

Inform marketing strategies

Sentiment analysis of television content is critical for developing marketing strategies. It provides businesses with insights into how the public perceives their brand, how successful their current marketing strategies are, and the impact of their competitors' marketing initiatives.

Utilising this knowledge enables businesses to improve the effectiveness of their marketing campaigns by allowing them to target their efforts more precisely, remain ahead of the curve, and alter their marketing plans as necessary.

How can 7th Minute help you conduct sentiment analysis?

Our tool is designed to help conduct sentiment analysis for TV. It is a specialised platform that analyses text data produced from TV content, such, using cutting-edge algorithms and natural language processing (NLP) methods.

The 7th Minute tool - 7M Discovery - transcribes every word said on TV and analyses this text data using natural language processing (NLP) methods. The tone and mood of the content are then determined and made available through an easy to use interface. The tool offers insights into  what’s being said during  TV content, including the context and sentiment of a topic. .

Additionally, our tool allows organisations to track and assess the influence of significant events on public opinion, such as sporting events and political debates. Researchers, decision-makers, and organisations can use this data to better understand the attitudes and opinions of the public and base their decisions on it.

If you want to learn more about how 7th Minute’s tool can help you make the most of your TV data, follow this link and sign up for the free version.

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