Social Media Sentiment Analysis Using Orange

Hello there, Will it be fine If I do using python? I am experienced in web scraping and to build script or a windows desktop application by using python. I am also experienced in large data scraping from the given website, by pass IP, Captcha, and anti bot or cloud flair protection. Please message me to discuss more regarding this project. Best Regards

<<< السلام عليكم >>> I'm sure that I can do this job. I'm artificial intelligence engineer experienced in NLP, Feature Engineering, Data Science, Statistics, Machine/Deep Learning. I have accomplished many projects like yours, Also I will arrange with you to have a session to provide full illustration for you. Here you can find some of my clients reviews (https://www.freelancer.com/u/mohamedabdelall4 ). Feel free to contact me for further details because I am looking forward working with you. Thanks

Hi, I have +7 years of experience dealing with machine learning algorithms and worked on multiple projects in this field, Please contact me to discuss more. Have a nice day

Hi I am Rashi I have completed a project earlier on Sentimental Analysis where I mrked the comments are Good, Bad and Neutral (positive/negative/neutral)according to the user comments. I classified them accordingly. I also do Data and Text annotation and can complete this task. Additionally I have a team to work on this. Please message to discuss further

Marhaban Sure, I can help you apply Orange for social media sentiment analysis. Orange is a great tool for performing sentiment analysis as it provides a user-friendly interface and has several built-in widgets and algorithms for text analysis. Here are the steps to perform sentiment analysis using Orange: Step 1: Collect and preprocess data The first step is to collect data from social media platforms such as Twitter, Facebook or Instagram. Make sure the data is in text format and is properly cleaned and preprocessed (removing stopwords, punctuation, numbers, etc.). Step 2: Load data into Orange Next, open Orange and load the preprocessed data into the TextIO widget. You can also use the Twitter widget to directly collect data from Twitter. Step 3: Generate word counts Use the Bag of Words widget to generate word counts for each document in the dataset. This will create a matrix of word frequencies. Step 4: Perform sentiment analysis To perform sentiment analysis, use the Sentiment Analysis widget. This widget provides different algorithms such as Naive Bayes, Support Vector Machines, and Maximum Entropy to classify the sentiment of each document (positive, negative or neutral). Step 5: Visualize sentiment analysis results Once the sentiment analysis is performed, you can use various widgets to visualize the results. For example, you can use the Scatter Plot widget to visualize the sentiment scores for different documents. The higher the score, the more positive the sentiment. Step 6: Interpret the results Finally, use the Text Explorer widget to analyze the sentiment of individual words and phrases within the documents. This will help you gain insights into the overall sentiment and identify any patterns or trends. In addition to these steps, you can also use other widgets in Orange such as Word Cloud, Topic Modeling, and Text Classification to further enhance your analysis. Play around with the different options and parameters to get the best results. I hope this helps you apply Orange for social media sentiment analysis. If you have any further questions, feel free to reach out to me. Good luck! Best regards, Giáp Văn Hưng

Data mining and machine learning, particularly in the context of natural language processing, have been at the core of my professional journey. Over the years, I have learned to channel these skills into tangible outcomes, with a primary focus on sentiment analysis using Orange tool. My experience covers various social media platforms but I have dedicated a significant amount of time to understand the nuances of sentiment in Facebook comments – the very task required for your project. I am cognizant of how crucial accurate sentiment classification is for interpreting and acting on feedback. With this understanding, my analytical approach towards clustering, labelling sentiments, and generating relevant reports aligns well with your project requirements. Furthermore, as an engineer who prioritizes client satisfaction, I assure you of detailed reports that translate complex results into easily comprehensible insights. In summary, choosing me would mean opting for a skilled and dedicated professional who not only has an engaging knowledge of Orange but also understands how the interpretation of sentiment can help inform product improvement strategies. I am fully committed to providing you with results that aid in enhancing your product based on rigorous analysis of Facebook comments. Your project Plus my proficiency equals winning results!