Sentiment Analysis for Elections: Sentiment Analysis to predict political elections winning
Sentiment analysis, also known as opinion grabbing/mining, is referred to as the evaluation of what the opinion of the writer, speaker, or other subjects is concerning a topic. This is a tool that helps in understanding public views about a political candidate. Sentiment analysis is a part of natural language processing and machine learning.
This tool is used for categorizing opinions expressed in various platforms such as audits, news, and articles. The categorization can be damaging, neutral, or positive. This tool has begun to be used by political leaders for understanding the views of the public concerning their performances.
Using election prediction sentiment analysis, the chances of candidates can be understood. It also can be used as a guide to the voters who are unsure of which candidate they should vote for. In short, sentiment analysis helps in analyzing the emotional tone of public views that are expressed online.
In addition to categorizing opinions, sentiment analysis may also use sentiment scores for grading statements. This is a scaling system that helps in understanding the depth of emotions in a text. The scale may assign a specific value to the sentiment on a scale of 0 to 10. A value of 0 may imply the most optimistic view, while ten may represent the most negative opinion.
Sentiment Analysis for Elections
Types of Algorithms Used for Political Sentiment Analysis
- As sentiment analysis is a fully automated process, it uses specific algorithms to make accurate predictions. Rule-based systems are one of the essential types of algorithms that are used. This approach involves using human-made rules for determining the polarity in opinions.
- It compares lists of polarized words. If more positive comments appear, then the sentiment is regarded as positive. For an actual appearance of positive and negative stories, the result will be a neutral sentiment.
- The intuitive approach is another form of the algorithm used in sentiment analysis. This approach employs machine learning techniques to feed a classifier with a text that can yield categories such as negative, positive, or neutral.
- To achieve this, machine learning algorithms are fed with pairs of tags and feature vectors by a feature extractor.
- Sentiment analysis can also be done using hybrid systems.
- The focus point of this approach is combining both automatic and Rule-based techniques to make predictions that are more accurate and reliable.
Benefits of Election Prediction using Sentiment Analysis
Sentiment analysis can help in the prediction of election results in numerous ways. Some of the most apparent usefulness that has been identified so far are mentioned for you.
- Sentiment analysis is done through automated tools that use algorithms to interpret data. This means there is zero need for manual intervention. As a result, this is an extremely time-saving method for sorting thousands of data and predicting election results.
- Sentiment analysis provides an insight into public attitudes towards a topic. This knowledge can, in turn, be used as areas that require suitable actions for the development of political parties.
- The growing popularity of sentiment analysis as natural language processing and machine learning technologies, and artificial intelligence turned it a common topic of research. And this makes the application of sentiment analysis a need for operating with advanced technologies.
- The real-time analysis of public sentiments can help in the immediate identification of areas that need attention. Once identified, the necessary actions can be implemented in real-time without causing any delay and further manipulation of voter’s views.
Limitations of Sentiment Analysis for Elections
Despite the various advantages that sentiment analysis brings with it, some challenges should not be overlooked. Some of the few limitations are mentioned below:
- Biases in the prediction of opinions that are suitable for specific contexts. For example, the tone of the response “absolutely nothing” can change if the question changes from “What did you like about the event?” to “What did you dislike?”
- Inaccurate prediction of sarcastic views that involves positive words for negative expression.
- It is analyzing emojis that are used extensively on social media platforms such as tweeter.
- Incorrect categorization of neutral sentiments for views that involve objective texts that are devoid of exact emotions, irrelevant information, or vague opinions such as “I wish it were better.”
- Dependency on a data feed, which has to be error-free for the machine to make correct predictions.
- Difficulty in evaluating views containing contrastive conjunctions such as “the weather was bad, but the event was great.”
- Recognition of named-entities in different contexts.
Election Prediction Using Twitter Sentiment Analysis Code
Social media plays a role in shaping your attitude towards various objects is immense. A large part of daily life is devoted to accessing different social media platforms. The constant flow of ideas and information can form or change your attitude.
And this is why analyzing public sentiments expressed in such platforms is an essential application of sentiment analysis.
Sentiment analysis using social media can be done by monitoring the mentions of your campaign or party on social media. For instance, in the past US presidential elections, the number of negative comments of candidates in news reports and media was analyzed.
Paying close attention to your social media channels can also help understand the success of the campaign in reaching people.
And finally, you can analyze Facebook posts and tweets. Tweeter offers a practical and fast way of analyzing public sentiments towards political parties and candidates. To use this platform, you first need to request Twitter API for fetching data related to your query.
You can use TextBlob for POS, or part of speech, tagging to leave you with the significant features. TextBlob is the library that python uses for processing textual data.
TextBlob will allow election data analysis using python. The text can then be fed to a sentiment classifier that will classify the tweet sentiment as negative, positive, or neutral.
Sentiment analysis is, thus, one of the most effective means in today’s world for understanding public views. This user-friendly method has several applicabilities, such as business, customer service, marketing, and politics. The ability of this technique for making election predictions is drawing the attention of researches and practical uses on a large-scale.
LSI Keywords for Political Sentiment Analysis
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