A Step-by-Step Tutorial for Conducting Sentiment Analysis

It is estimated that 80% of the world’s data is unstructured. Thus deriving information from unstructured data is an essential part of data analysis. Text mining is the process of deriving valuable insights from unstructured text data, and sentiment analysis is one applicant of text mining. It is using natural language processing and machine learning techniques to understand and classify subjective emotions from text data. In business settings, sentiment analysis is widely used in understanding customer reviews, detecting spams from emails, etc. This article is the first part of the tutorial that introduces the specific techniques used to conduct sentiment analysis with Python. To illustrate the procedures better, I will use one of my projects as an example, where I conduct news sentiment analysis on WTI crude oil future prices. I will present the important steps along with the corresponded Python code.

#machine-learning #python #sentiment-analysis #text-mining #data-science

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Sentiment Analysis in Python using Machine Learning

Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?

We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.

But why do we need sentiment analysis?

Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.

It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.

Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.

Lastly, they are also used for spam filtering and removing unwanted content.

How does sentiment analysis work?

NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.

NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.

Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.

Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are

• Simple classification of text into positive, negative or neutral. It may also advance into fine grained answers like very positive or moderately positive.
• Aspect-based sentiment analysis- where we figure out the sentiment along with a specific aspect it is related to. Like identifying sentiments regarding various aspects or parts of a car in user reviews, identifying what feature or aspect was appreciated or disliked.
• The sentiment along with an action associated with it. Like mails written to customer support. Understanding if it is a query or complaint or suggestion etc

Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.

• Matching rules based sentiment analysis: There is a predefined list of words for each type of sentiment needed and then the text or document is matched with the lists. The algorithm then determines which type of words or which sentiment is more prevalent in it.
• This type of rule based sentiment analysis is easy to implement, but lacks flexibility and does not account for context.
• Automatic sentiment analysis: They are mostly based on supervised machine learning algorithms and are actually very useful in understanding complicated texts. Algorithms in this category include support vector machine, linear regression, rnn, and its types. This is what we are gonna explore and learn more about.

In this machine learning project, we will use recurrent neural network for sentiment analysis in python.

#machine learning tutorials #machine learning project #machine learning sentiment analysis #python sentiment analysis #sentiment analysis

R Tutorial: Better Blog Post Analysis with googleAnalyticsR

In my previous role as a marketing data analyst for a blogging company, one of my most important tasks was to track how blog posts performed.

On the surface, it’s a fairly straightforward goal. With Google Analytics, you can quickly get just about any metric you need for your blog posts, for any date range.

But when it comes to comparing blog post performance, things get a bit trickier.

For example, let’s say we want to compare the performance of the blog posts we published on the Dataquest blog in June (using the month of June as our date range).

But wait… two blog posts with more than 1,000 pageviews were published earlier in the month, And the two with fewer than 500 pageviews were published at the end of the month. That’s hardly a fair comparison!

My first solution to this problem was to look up each post individually, so that I could make an even comparison of how each post performed in their first day, first week, first month, etc.

However, that required a lot of manual copy-and-paste work, which was extremely tedious if I wanted to compare more than a few posts, date ranges, or metrics at a time.

But then, I learned R, and realized that there was a much better way.

In this post, we’ll walk through how it’s done, so you can do my better blog post analysis for yourself!

What we’ll need

To complete this tutorial, you’ll need basic knowledge of R syntax and the tidyverse, and access to a Google Analytics account.

Not yet familiar with the basics of R? We can help with that! Our interactive online courses teach you R from scratch, with no prior programming experience required. Sign up and start today!

You’ll also need the dyplr, lubridate, and stringr packages installed — which, as a reminder, you can do with the install.packages() command.

Finally, you will need a CSV of the blog posts you want to analyze. Here’s what’s in my dataset:

post_url: the page path of the blog post

post_date: the date the post was published (formatted m/d/yy)

category: the blog category the post was published in (optional)

title: the title of the blog post (optional)

Depending on your content management system, there may be a way for you to automate gathering this data — but that’s out of the scope of this tutorial!

For this tutorial, we’ll use a manually-gathered dataset of the past ten Dataquest blog posts.

#data science tutorials #promote #r #r tutorial #r tutorials #rstats #tutorial #tutorials

A Step-by-Step Tutorial for Conducting Sentiment Analysis

Following the steps from my previous articles, I preprocessed the text data and transformed the “cleaned” data into a sparse matrix. Please follow the links to check out more details.

Now I am at the last step of conducting news sentiment analysis on WTI crude oil future prices. In this article, I will discuss the use of logistic regression, and some interest results I found from my project. I have some background introduction to this project here.

Define and Construct the Target Value

As discussed briefly in my previous articles, conducting sentiment analysis is solving a classification problem (usually binary) by machine learning models and text data. Solving a classification problem is solving a supervised machine learning problem, which requires both features and target values when training the model. If it is a binary classification problem, the target values are usually positive sentiment and negative sentiment. They are assigned and detailedly defined depending on the context of your research question.

Take my project as an example, the purpose of my project is to predict the change of the crude oil future prices from recently released news articles. I define the positive news as the ones that would predict the price increase, while the negative ones would predict the price decrease. Since I have already collected and transformed the text data and will use them as the features, I now need to assign the target values for my dataset.

The target value of my project is the directions of price change with respect to different news articles. I collected the high-frequency trading data from Bloomberg for the WTI crude oil future close price, which is updating every five minutes.

#machine-learning #gridsearchcv #logistic-regression #sentiment-analysis #data-science

A Step-by-Step Tutorial for Conducting Sentiment Analysis

It is estimated that 80% of the world’s data is unstructured. Thus deriving information from unstructured data is an essential part of data analysis. Text mining is the process of deriving valuable insights from unstructured text data, and sentiment analysis is one applicant of text mining. It is using natural language processing and machine learning techniques to understand and classify subjective emotions from text data. In business settings, sentiment analysis is widely used in understanding customer reviews, detecting spams from emails, etc. This article is the first part of the tutorial that introduces the specific techniques used to conduct sentiment analysis with Python. To illustrate the procedures better, I will use one of my projects as an example, where I conduct news sentiment analysis on WTI crude oil future prices. I will present the important steps along with the corresponded Python code.

#machine-learning #python #sentiment-analysis #text-mining #data-science

A Step-by-Step Tutorial for Conducting Sentiment Analysis

The process includes tokenization, removing stopwords, and lemmatization. In this article, I will discuss the process of transforming the “cleaned” text data into a sparse matrix. Specifically, I will discuss the use of different vectorizers with simple examples.

Before we get more technical, I want to introduce two terminologies that are widely used in text analysis. For a collection of text data we want to analyze, we call it corpus. A corpus contains several observations, like news articles, customer reviews, etc. Each of these observations is called a document. I will use these two terms from now on.

The transformation step works as building a bridge that connects the information carried in the text data and the machine learning models. For sentiment analysis, to make sentiment predictions on each document, the machine learning model needs to learn the sentiment score of each unique word in the document, and how many times each word appears there. For example, if we want to conduct sentiment analysis for customer reviews of a product, after training the model, the machine learning models are more than likely to pick up the words like “bad”, “unsatisfied” from negative reviews, while getting words like “awesome”, “great” from positive reviews.

Facing a supervised machine learning problem, to train the model, we need to specify features and target values. Sentiment analysis is solving a classification problem, and in most cases, it is a binary classification problem, with target values defined as positive and negative. The features used to the model are the transformed text data from a vectorizer. The features are constructed differently with different vectorizer. In Scikit Learn, there are three vectorizers, CountVectorizer, TFIDFVectorizer, and HashingVectorizer. Let’s discuss the CountVectorizer first.

#count-vectorizer #sentiment-analysis #data-science #machine-learning #tfidf-vectorizer