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Intro

Do you remember how confusing and stressful it felt when you filled out your first tax form, created your first bank account, or entered into your first complicated rental contracts?

Often it can feel like we all go through this “adulting” journey on our own. Curiously, few essential life skills are ever taught in our formal education: young adults often lack the knowledge and information to successfully transition into adulthood, a phenomenon widely known, understood, and even mocked as “adulting”.

In the previous two posts, we showed that current online education platforms do not provide necessary adulting knowledge in a manner most conducive to learning. We also identified top concerns in adulting and finance from our bigram frequency analysis. Now, we want to learn about people’s feelings towards these adulting challenges to understand their needs and come up with more optimal solutions. As such, we conducted sentiment analysis and topic modeling summarized in this article to investigate adulting concerns in detail.

Key Takeaways

To identify the most emotionally-charged adulting concern, we extracted texts that contained interesting keywords we obtained from our bigram frequency. By employing NRC Emotion Intensity Lexicon (NRC-EIL), a dictionary of 9922 lemmas associated with emotional intensity, we came up with the radar plot below on the seven keywords we chose.

*Rescaled: Using same sample size for each keywords

Insights:

To explore the dominant emotion under each keyword, we rescaled each emotion by dividing the total emotional words a keyword has. Key observations are summarized in the table. We can see that “parent” is highly correlated to negative emotions.

Below are the word clouds for each emotional category. They are here to clarify what each category is about and help us to understand the meaning of the insights above.

To understand the topics under each emotion, we labelled each post as anger, trust, surprise, sadness, anticipation, joy, fear, or disgust by adding up the sentiment coefficient of each sentimental word used in a given post and picking the one with the highest result. (For example, “I was angry with you, but now I forgive you.” has “angry” and “forgive”. Whether the sentence will be labelled as “anger” or “trust” depends on which word got a higher sentiment “score”. )

After labeling, we utilized topic modeling, the process of logically allocating words that belong to a certain topic to the same cluster. We used it here to understand unique and shared topics under the eight emotions.

For Adulting:

Unique topics under each emotion:

Shared topics across emotions:

Among these topics, we find the result of parents and mental health to be interesting. Though the top 3 emotions of “parents’’ have sadness and fear, it is also highly related to trust. Such a mixed bag of emotions is worthy of further investigation.. Meanwhile, it is interesting to see social media associated with both fear and joy, while sadness and fear surround mental health issues. Because the age of 18 is not only a turning point into adulthood but also the first year of college life, we selected “College” as the keyword to analyze mental health issues in detail.

For Finance:

Unique topics under each emotion:

Shared topics across emotions:

Among these topics, we can see that credit cards are of special interest. It has mixed sentiment of fear, disgust, and trust; credit reports and collection agencies are of sadness and trust; credit card account service is under anger. We thus chose “Credit” as a keyword for further analysis in this report.

To understand the mixed sentiment of keywords “college”, “parent”, and “credit”, we conduct topic modeling to see how their subtopics relate to different emotions. (We conducted topic modeling on more topics but we selected these three topics here that are most coherent with the last two posts and representative of the general research goal.)

Topic 0: “good credit”, “affect credit”, “credit score”

Topic 1: “pay credit”, “card debt”, “credit card”

Topic 2: “account”, “loan”

Insights:

1.The discussion about credit is generally positive. The most popular discussions were about advice on the usage of credit cards and people sharing their experience improving their credit scores.

2.Topic 0 is about credit scores and ways to get a good credit score which include mixed feelings. This is more associated with “hurt” and “excellent”.

3.Topic 1 is about choosing and paying back credit cards. It is more associated with “ill” and “worry” with a relatively lower sentiment score.

4.Topic 2 is about bank accounts and loans, and people discuss how to get approval on them. This is more associated with “rejected” and “approved”.

Topic 0: “currently”, “20 year old”, “college student”

Topic 1: “ graduated college”, “recently graduated”, “year ago”

Topic 2: “money”, “job”, “time”

Insights:

1.All topics have a medium around zero, and most of the bar is around zero or above, indicating a generally positive trend. Through topic modeling, we split the keyword college to two age groups. The group of recent graduates who are looking for jobs and paying college have more variance in their sentiment score.

2.Topic 0 is about expectations of college life and confusion about the future. It is more associated with joyful words like “opportunity” , “start”, “advice”.

3.Topic 1 is about getting a first job and dealing with moving out. It is more associated with “independence”, “explore”, and “failure” in the adulting journey.

4.Topic 2 is about general college experience with high mentioning of its cost and mental health issues. It is more associated with “lost” and “depressed”.

Topic 0: “money”, “house”, “time”

Topic 1: “bank”, “company”, “credit”, “account”

Topic 2: “currently living with parents”

Insights:

1.Topic 0 has the highest sentiment score, Topic 1 is mixed with large sentiment variance, and Topic 2 is the most negative. We can infer that living with parents can be challenging for many young adults.

2.Topic 0 is about the worries in money and house with parents, which is more associated with “love” and “planning”compared to the other two topics. It shows a supporting parental relationship.

3.Topic 1 is about the confusion in banking issues after leaving parents. This is more associated with “fidelity”, “friend” , “trust”, “frustrated”, “pain”, and “lost” compared to the other two topics.

4.Topic 2 is about living with parents. It is more associated with “battle”, “guilty”, and “depression”, which demonstrate negative examples of parental relationships.

In the three-posts-series of this project, we showed the weakness of current online education platforms in providing necessary adulting knowledge. We also identified top concerns in adulting and finance from our bigram frequency analysis. Finally, we explored people’s feelings towards these issues to understand their needs. We super appreciated the initiative raised by onomy and their support throughout this project. We hope our insights could make onomy better in building a great learning platform for adulting challenges.

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