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Sentiment Analysis is a tool utilized in text mining. Therefore, Twitter Sentiment Analysis means that it’s a tool to analyze mined text on Twitter. However, what is it and how does it work? Sentiment Analysis is in another way called Opinion Mining, it is a tool that works to understand whether the overall text is generally positive, negative, or neutral. In our case, this is all in the form of public tweets. It is utilized to decide the business strategy, assessing public actions, and political analysis. It is most common with financial market enthusiasts as Twitter is a very important influencer on the general prices of financial instruments. Twitter is the most favorite global platform that has been used for several purposes like entertainment, online marketing and so on. To get higher amounts of followers people often find ways to buy Twitter followers.
Twitter’s importance is so huge that even the top 100 forex brokers in the world agreed to make it a universal tool. There are numerous applications providing this service like Enginuity, Revealed Context, Steamcrab, MeaningCloud, and SocialMention
These tools are written in Python or R programming languages, which are extremely popular in the data analysis sphere. Python, in particular, is one of the best tools to utilize for data analysis and has extremely useful already embedded libraries which the programmer just imports. Twitter Sentiment Analysis tools are extremely useful and complicated. They require special training to be used and are widely spread due to the fact that Twitter is freely allowing the integration of its API into independent projects.
Using Sentiment Analysis for Forex Trading
As we already mentioned Sentiment Analysis is a tool used during text mining. It is one of the most versatile programs for a lot of different interested parties starting from political ending with marketing businesses. The Twitter sentiment is an emotion expressed through twits. The tool analyses the text and tries to understand whether the keywords used in the post are utilized in a positive, negative, or neutral manner thereby giving the interested parties an ability to survey the society and understand the overall sentiment about the product. However, as a disclaimer, it is worth noting that the algorithm cannot physically give 100% accurate results, and thus the outcome should be taken with a grain of salt. Although, even in the face of inaccuracy companies utilize this tool for marketing purposes, constantly scanning twitter to understand the tendency and feel of people in connection to their product. Twitter is one of the most important tools for traders as that’s exactly the place where most of them get their information about market preferences. This is why Forex brokers utilize Twitter as one of their main sources of information to assess and analyze what people’s outlook is about specific assets. This is an extremely good way to determine whether the market for the asset will shrink or grow.
Natural Language Processing, or NLP, is a subfield of linguistics, computer science, and information engineering, as well as, artificial intelligence studies that work on the interaction between computers and human (natural) languages. This is exactly what the Twitter Sentiment Analysis tool is. It is using algorithms like SVM, Naive Bayas, and etc. to predict the polarity of the sentence itself. The inaccuracy comes from the natural aspects of human conversation. In general, the positive or negative aspect comes from the realization of the actual context of the sentence as well as the knowledge of the person as a whole. Emotional bases have a huge impact on overall idea and thus individual twits may not be the best indicators of the overall idea about the said subject.
Who Uses Twitter Sentiment Analysis?
The applications are numerous starting from businesses, politics, public actions, and whatnot.
Companies or businesses utilize this tool to better develop their business strategies, assess customer attitude towards the product, and overall response towards their brand items like campaigns and product launches.
In politics, the sentiment analysis dataset is used to keep track of people’s views about certain politicians, ideology, or even a concept. It is highly used to detect the consistencies and inconsistencies between government level actions and statements. Election results are also being analyzed using tools like this not only on Twitter but on Facebook and every other social platform as well.
Public Actions can also be analyzed by such tools to prevent any kind of dangerous actions which may be conspired by dangerous groups or emotional responses from the citizens of any country.
How to Trade FX with Twitter Sentiment Analysis
As it was already mentioned Twitter Sentiment Analysis is done using either R or Python programming languages. In Python, there are specific libraries like Tweepy and TextBlod, which assist with this endeavor.
Tweepy is a Python client, which fully supports the Twitter API, which accesses twitter via basic authentication and the newer method called OAuth. It is worth noting that Twitter as a company stopped accepting basic authentication so OAuth is the only way to proceed with Tweepy as of now.
TextBlob is one of the most popular Python libraries for processing textual data. It is built on NLTK and works as a framework for all required tasks that are needed for NLP.
