Sentiment Analysis with Insights using NLP and Dash This project show the sentiment analysis of text data using NLP and Dash. I used Amazon reviews dataset to train the model and further scrap the reviews from Etsy.com in order to test my model. Prerequisites: Python3 Amazon Dataset (3.6GB) Anaconda How this project was made? This project has been built using Python3 to help predict the sentiments with the help of Machine Learning and an interactive dashboard to test reviews. To start, I downloaded the dataset and extracted the JSON file. Next, I took out a portion of 7,92,000 reviews equally distributed into chunks of 24000 reviews using pandas. The chunks were then combined into a single CSV file called balanced_reviews.csv. This balanced_reviews.csv served as the base for training my model which was filtered on the basis of review greater than 3 and less than 3. Further, this filtered data was vectorized using TF_IDF vectorizer. After training the model to a 90% accuracy, the reviews were scrapped from Etsy.com in order to test our model. Finally, I built a dashboard in which we can check the sentiments based on input given by the user or can check the sentiments of reviews scrapped from the website. What is CountVectorizer? CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for using in further text analysis). CountVectorizer creates a matrix in which each unique word is represented by a column of the matrix, and each text sample from the document is a row in the matrix. The value of each cell is nothing but the count of the word in that particular text sample. What is TF-IDF Vectorizer? TF-IDF stands for Term Frequency - Inverse Document Frequency and is a statistic that aims to better define how important a word is for a document, while also taking into account the relation to other documents from the same corpus. This is performed by looking at how many times a word appears into a document while also paying attention to how many times the same word appears in other documents in the corpus. The rationale behind this is the following: a word that frequently appears in a document has more relevancy for that document, meaning that there is higher probability that the document is about or in relation to that specific word a word that frequently appears in more documents may prevent us from finding the right document in a collection; the word is relevant either for all documents or for none. Either way, it will not help us filter out a single document or a small subset of documents from the whole set. So then TF-IDF is a score which is applied to every word in every document in our dataset. And for every word, the TF-IDF value increases with every appearance of the word in a document, but is gradually decreased with every appearance in other documents. What is Plotly Dash? Dash is a productive Python framework for building web analytic applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python. Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready. Dash is an open source library, released under the permissive MIT license. Plotly develops Dash and offers a platform for managing Dash apps in an enterprise environment. What is Web Scrapping? Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Running the project Step 1: Download the dataset and extract the JSON data in your project folder. Make a folder filtered_chunks and run the data_extraction.py file. This will extract data from the JSON file into equal sized chunks and then combine them into a single CSV file called balanced_reviews.csv. Step 2: Run the data_cleaning_preprocessing_and_vectorizing.py file. This will clean and filter out the data. Next the filtered data will be fed to the TF-IDF Vectorizer and then the model will be pickled in a trained_model.pkl file and the Vocabulary of the trained model will be stored as vocab.pkl. Keep these two files in a folder named model_files. Step 3: Now run the etsy_review_scrapper.py file. Adjust the range of pages and product to be scrapped as it might take a long long time to process. A small sized data is sufficient to check the accuracy of our model. The scrapped data will be stored in csv as well as db file. Step 4: Finally, run the app.py file that will start up the Dash server and we can check the working of our model either by typing or either by selecting the preloaded scrapped reviews.

The Applied Data Science program at Syracuse University's School of Information Studies provides students the opportunity to collect, manage, analyze, and develop insights using data from a multitude of domains using various tools and techniques. In courses such as Database Administration, Data Analytics, Text Mining, and Marketing Analytics, reports and presentations were developed to deliver insights using Microsoft Access, SQL Server Management Studio, Python, R, Excel, and Tableau. The skills developed at the School of Information Studies furnish data scientists focused in the field of marketing analytics with the ability to generate value within their organizations and produce actionable recommendations.

From the book of the same title

An Alteryx tool for text analytics, written with the Python SDK. Built with lots of help from "Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning" by Benjamin Bengfort, Rebecca Bilbro, and Tony Ojeda.

In at present's aggressive business surroundings, Data evaluation helps to assemble optimum Data and utilize it in each sector; be it a small begin-up or an established group. After present process this course the members will develop into data engineers who can carry out analytics operations on Data utilizing various instruments. Sentimental Analytics: Creating Sentimental Analytics by Downloading the Tweets from Twitter and Feeds the trending Data to the Application. Today, Big Data analytics are producing invaluable insights in almost all enterprise functions, whether it is advertising evaluation, forecasting worker attrition or making strategic selections. The key parameters primarily based on which these coaching institutes have been ranked comprised in fact content material & comprehensiveness, college details comparable to those with PhD or business experience, scholar experience resembling post completion engagement. Effectively, with our coaching, members can excel of their profession as a Data Analyst and statistics will stand as proof of our quality services. Imarticus Studying has also established alliances with global analytics companies namely, Genpact and DXC Technology. We bring to you the analytics and data science training institute ranking for the 12 months 2018. As a part of this course, find out about Textual content analytics, the various textual content mining techniques, its utility, text mining algorithms and sentiment analysis. The institute aims to address the challenges within the industry and empower professionals through their transformational programs in Huge Data and Data Science. They offer coaching in multiple common and niche instruments resembling Data science, machine studying, large Data and AI applied sciences. We provide on-line large ExcelR Data analytics courses and certification program so you possibly can be residing wherever in India, like Delhi, Gurgaon, Bangalore, Mumbai, Hyderabad, Pune and so forth. Description: Be taught in regards to the other moments of business determination as a part of Statistical Analysis Study extra about Visual Data representation and graphical techniques. Find out about Python, R programming with respect to Data Science and Machine Learning Understand the best way to work with different Python IDE and Python programming examples.

Descriptions Twitter allows elected public officials to communicate with millions of other people over the internet. With each tweet, 280 characters are given to compose a message that will ultimately influence the public’s perception of that individual. Since 535/535 members of Congress have twitter handles and consistently tweet, this public information can be analyzed to gain insights on where a politician stands on a policy issue. For this project, we will be analyzing the tweets of all members of Congress during the height of the Black Lives Matter and George Floyd protests between May 26th and June 30th. We aim to determine if there was a difference in the language used by members of Congress from different parties throughout the protests. Source Data For this project, we are going to leverage Alex Litel’s Github Repository ‘congresstweets’, which automatically downloads the tweets of members of Congress, as our main data source. This data is collected nightly, but we will be using the data from May 26th through June 30th, as this was the height of the Black Live Matter and George Floyd protests. The data is provided as JSON files, which we will process using Python on Google Colab, accessed from a shared Google Drive folder. The JSON files between our chosen dates are 55.6MB. The data is licensed under MIT’s Open Source Licensing, found at the link below. Source links: https://github.com/alexlitel/congresstweets https://opensource.org/licenses/mit-license.php Questions to be Answered - Are there any members of Congress whose tweets differ from those of others in their parties. - What do tweets tell us about the messaging of a political party? - Do attributes like subjectivity and polarity significantly differ between Republicans and Democrats? - How does this change over time? - What other context can this text analytics method be applied to? - Can we detect similar trends of tweet behavior during the election? What about during impeachment?

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