Predict Mood from Social Media Data

@Vasilchenko

Hi, I can analyze your survey dataset and build a reproducible model to predict next-day mood from today’s social media usage (hours + any other features), with proper validation to avoid leakage and clear performance metrics. You’ll get a well-commented Python/R/SPSS notebook plus an easy-to-read summary explaining which variables drive predictions and what actionable thresholds/patterns you can use. https://www.freelancer.com/u/Vasilchenko

@LiveExperts

Hello, As a team of engineers and developers adept in data analysis, including Machine Learning, Artificial Intelligence, Big Data, and Statistical Analysis using tools such as R programming and SPSS Statistics - we are exactly what you need for your project. Our forte lies in distilling complex datasets into valuable insights that drive action. Our professional track record reflects the reliability and precision we promise to bring to the table. We understand the importance of your project's requirements, and through our extensive experience in numerous fields of engineering and software development, we can ensure every aspect is handled with utmost diligence. With our expertise in prediction and classification analytics, allowing us to derive actionable insights accurate enough to predict tomorrow's moods based on today's social media usage. Our proficiency in handling mixed datasets enables us to tackle your project seamlessly. When you choose Live Expert®, you're choosing high-quality work ethic that values your satisfaction above all else. Let's transform your idea into reality- contact us now! Thanks!

@HiraMahmood4072

Hi, I hope you're doing well. I understand you're looking for Predict Mood from Social Media Data I am the ideal candidate for your project. I have read the provided job description and I understand what you are looking for. I have over 10+ years of experience Statistics, R Programming Language, Statistical Analysis, SPSS Statistics, Data Science, Data Analytics, Data Analysis, Predictive Analytics .Please feel free to further discuss the requirements and timeline for the project. I'd be happy to assist you. I am ready to start right now. ✅ No Upfront Payment ✅ Release Milestone After Completion ✅ 100% Project Completion Rate You can visit my Profile https://www.freelancer.com/u/HiraMahmood4072 Thank you

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Statistics, R Programming Language, Statistical Analysis, SPSS Statistics, Data Science, Data Analytics, Data Analysis, Predictive Analytics and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@adnank30

As someone who specializes in data analysis and interpretation, I can confidently assure you that I would be a valuable asset for your project. I've spent significant time delving into complex datasets and have a solid understanding of how to leverage them to draw meaningful insights. My experience with mixed data sets, like the one you've mentioned, has only honed my skills further. Moreover, my holistic digital skill set brings an extra layer of value to the table. In addition to my strong foundations in data analysis, I also hold expertise in SEO, content strategy, and marketing. This unique blend ensures that my recommendations aren't just statistically sound, but also strategically aligned with your overall business goals. I'm excited about the prospect of helping you predict mood from social media data and look forward to delivering results thAs someone who specializes in data analysis and interpretation, I can confidently assure you that I would be a valuable asset for your project. I've spent significant time delving into complex datasets and have a solid understanding of how to leverage them to draw meaningful insights. My experience with mixed data sets, like the one you've mentioned, has only honed my skills further. Regards, Adnan.

@maxscender

I can help you. To get an accurate prediction, we must address the "baseline" problem: mood scores are subjective, so I will apply within-person normalization to ensure individual temperaments don't skew the model. A significant hurdle in this type of daily survey data is autocorrelation—today’s mood is often the strongest predictor of tomorrow’s—meaning we need to treat "previous mood" as a lagged control variable to isolate the actual impact of social media. I’ll implement a Mixed-Effects Model or a Gradient Boosting approach to capture the non-linear "tipping point" where usage hours move from neutral to negative, providing you with specific usage thresholds rather than just general correlations.

@devanshkhandekar

Hi, I am a seasoned Appled ML Engineer (6+ YOE) & I can help you build a solid,interpretable model to predict next-day mood from today’s social media usage & deliver actionable insights Relevant experience: >>Built predictive models onsurvey + behavioral mixed datasets (ordinal mood scales, time-use hours, demographics) with strong emphasis on leakage control and interpretability. >>Delivered analysis where stakeholders needed “what to do next”: feature impact, scenario simulations (e.g., +1 hour usage), and clear reporting. >>Handled common survey issues: missingness patterns, class imbalance, ordinal targets, and repeated measures (day-to-day logs). What I’ll deliver Clean EDA: distributions, missing data, outliers, and correlations (including non-linear checks). Modeling options based on your label type: >>If mood is ordinal (1–5/1–7):ordinal regression / calibrated gradient boosting >>If binary (good/bad): logistic/XGBoost with calibration >>If continuous score: regularized regression / tree models Proper validation: time-aware split (no future leakage), metrics (MAE/F1/AUC), and error analysis. Insights: key drivers, partial dependence/SHAP summaries, and practical recommendations (thresholds, risk bands). Deliverables: a notebook + cleaned dataset pipeline + short report with findings and “next steps”.

@Lazarone1

Hi, that’s great to hear! Your project closely aligns with one I recently completed. In that project, I built a mood‑forecasting pipeline using mixed behavioral datasets, regression models, and time‑series techniques with R, SPSS, and advanced statistical modeling. Your goal of predicting next‑day mood based on today's social media usage fits perfectly with my experience in handling survey data, modeling behavioral patterns, and extracting clear, actionable insights. I can structure the analysis to model mood variation, validate predictive accuracy, and deliver insights that help you understand the drivers influencing mood changes. I’d be glad to connect and share my experience in more detail over chat. Thank you. Best regards, Lazar

@anangf52

I deliver structured and reliable predictive data analysis focused on accuracy, clarity, and practical outcomes. I analyze mixed datasets combining mood survey data and social media usage, clean and validate inputs, engineer relevant features, and apply robust predictive modeling techniques to forecast next-day mood patterns. My approach ensures well-tested, explainable models with clear identification of behavioral drivers, data trends, and actionable insights that support informed decision-making. I present results in concise, well-structured reports and clear visual dashboards so insights can be easily interpreted and applied. I work comfortably with Python, statistical modeling, machine learning pipelines, and data visualization tools, and adapt quickly to different workflows and data environments. My priority is to deliver precise predictions, transparent methodology, and practical insights aligned with European professional standards.

@juanjosec63

Hello, I’m excited about the opportunity to contribute to your project. With my expertise in predictive analytics, statistics, mixed-data modeling, and implementation in R (tidyverse, caret/tidymodels) or SPSS, I can deliver a solution that aligns perfectly with your goals. I’ll tailor the work to your exact requirements, cleaning and exploring the survey dataset, building and validating next-day mood prediction models (e.g., regression/classification with regularization and tree-based baselines), and translating results into clear, actionable insights with interpretable drivers and practical recommendations. You can expect clear communication, fast turnaround, and a reproducible analysis package with reporting-ready outputs that fits seamlessly into your existing workflow. Best regards, Juan

@prathmesh276

Hi there, I can absolutely deliver this predictive analysis. Your project is a perfect fit for my expertise in transforming mixed survey data into clear, actionable insights. I don't just build models; I build understanding. Here is my streamlined approach to ensure you get accurate predictions and real value: 1. Data Deep Dive: I'll start by exploring your data to understand the relationship between social media habits and mood, handling the mixed data types (numbers + categories) expertly. 2. Smart Prediction: I will engineer powerful features (like usage patterns) and benchmark top models—including Random Forest and XGBoost—to find the most accurate predictor for next-day mood. 3. Actionable Insights: This is the most important part. I will provide you with a clear, non-technical report detailing: What drives mood: Which platforms or usage times are the biggest predictors. Scenario analysis: What happens to mood if usage is cut by 1 hour? Clear recommendations: A data-backed roadmap for improvement. I focus on clarity and results. I have two quick questions to refine our approach: Is the mood scored numerically (e.g., 1-10) or as categories (Happy/Sad)? Does the data include weekend vs. weekday usage? I am ready to start immediately and deliver insights you can actually use. Best Regards' [Prathmesh]

@YoussefBaryoul

Hello, I have previously worked on similar predictive analysis projects involving mixed datasets (behavioral data, usage metrics, and self-reported survey inputs), so this task feels very familiar to me. My approach will be structured and rigorous. I will start with thorough Exploratory Data Analysis (EDA), including correlation analysis, feature distributions, and identification of key patterns between mood and social media usage. Then, I will perform strong feature engineering to extract the most informative variables from the dataset. After carefully understanding and preparing the data, I will develop and fine-tune suitable Machine Learning / Deep Learning models to achieve reliable and accurate next-day mood predictions. Alongside the model, I will provide clear, actionable insights that explain how social media usage influences mood patterns. I am confident I can deliver a well-engineered pipeline and an accurate predictive model based on solid data analysis practices. Best regards, Youssef Baryoul

@rana0487

I am confident I can help you build a predictive model to forecast next day’s mood based on today’s social media usage. I have experience analyzing mixed datasets containing numerical and categorical data, and I am proficient in predictive modeling techniques such as regression, decision trees, and ensemble methods. I will clean and preprocess your survey data, explore patterns between mood and social media activity, and develop an accurate model to provide actionable insights. Additionally, I will provide clear visualizations and a concise report to help you understand and apply the findings effectively.

@dbdxn

I recently completed a project on analyzing customer support conversations via twitter. This project included identifying sentiment for each conversation, which facilitated identification of disgruntled customers. As a result we were able to identify sentiment-related metrics such as: - most disgruntled customers - issues customers were most upset about - companies with the most negative conversations - industries with the most negative conversations I believe this previous project can be extended to your project. I am confident I can successfully complete this task for you.

@mahmoudmetaweix

Hello, I see you’re looking to predict next-day mood based on social media usage patterns. This is a very interesting behavioral modeling problem, and I’d approach it carefully to ensure both predictive accuracy and meaningful interpretation. My approach would include: • Cleaning and preprocessing the mixed dataset (handling missing values, encoding categorical variables) • Exploratory analysis to understand correlations and patterns • Feature engineering (e.g., lag variables, interaction effects if relevant) • Testing predictive models such as Logistic Regression, Random Forest, or Gradient Boosting • Evaluating performance using appropriate metrics (Accuracy, F1-score, RMSE depending on mood scale type) Beyond prediction, I focus strongly on interpretability — identifying which aspects of social media usage most influence next-day mood and translating that into actionable insights. I have experience building predictive models in Python using Pandas, Scikit-learn, and LightGBM, and presenting results clearly for non-technical audiences. Before proceeding, may I ask: Is the mood variable categorical (e.g., happy/sad/neutral) or continuous (scale 1–10)? That will determine the modeling approach. Looking forward to hearing more about the dataset. Best regards, Mahmoud