Answers.Body: <p>Given the complexity of your dataset and the potential relationships among words and entities, a transformer-based architecture, such as BERT (Bidirectional Encoder Representations from Transformers), could be a more appropriate choice.</p>
  <p>It's worth noting that the selection of the model ultimately depends on various factors, including the size of your dataset, computational resources available, and the desired level of accuracy. It would be beneficial to experiment with different models and evaluate their performance on a validation set before selecting the most suitable one for your task.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-11T07:02:14.430Z
Answers.LastActivityDate: 2023-12-11T07:02:14.430Z
Answers.UserDisplayName: han
Body: <p>I have a reddit dataset with thousands of online posts over the economy and inflation. We have used human-annotation on 60% of posts to determine whether users blame the following entities over the current inflation: We coded 1 for posts blaming Democrats, We coded 2 for posts blaming Republicans, We coded 3 for posts blaming the Federal Reserve, We coded 4 for posts that are negative about the economy/inflation but are not particularly blaming any political party or government entity over the economy.</p>
  <p>My goal is to use the human-annotated posts to determine who is being blamed in the remaining 50% of posts that have not been labelled, and I am not sure if using a naive bayes model is appropriate here or if other ML models are more suitable? I have seen some guides like <a href="https://towardsdatascience.com/sentiment-analysis-introduction-to-naive-bayes-algorithm-96831d77ac91" rel="nofollow noreferrer">this</a> that recommend using a naive bayesian model for cases like mine but I am not sure if they are suitable?</p>
  <p>#data example</p>
  <pre><code>dput(df[1:10,c(1,2,3,4,8)])
  </code></pre>
  <p>#data output:</p>
  <pre><code>
  structure(list(id = c(1933, 7161, 4661, 2885, 5102, 5052, 4585, 
  1935, 5259, 891), username = c(&quot;the_dog&quot;, &quot;Empyrean Cobalt&quot;, 
  &quot;Engineeer&quot;, &quot;AuraKUPO&quot;, &quot;kyo_465&quot;, &quot;Johnpierpontmorgan&quot;, &quot;ToughLuck&quot;, 
  &quot;SmallQuietDog&quot;, &quot;kyo_465&quot;, &quot;LessonsIndissent&quot;), post = c(&quot;Think jhk AT Pinoy etc all should be grateful to cecas for taking the heat off them&quot;, 
  &quot;Scamdetector said I hope you all realised that UOB actually didnt answer Clement question Click to expand UOB is just using the same template to reply like robotic mode&quot;, 
  &quot;netzach said this Daniels guy Click to expand I think people are not understanding WP and Daniel Goh in their entirely&quot;, 
  &quot;TS is still a student Companies must pay FT CPF in cash instead Only sinkies need to pay CPF a cut of their pay&quot;, 
  &quot;ponpokku said at least they dun downgrade like CECA pose no threats to locals CECA ish u have to double work to kio their sai Click to expand I dun want to hire people who tell me there are those who are worse than them&quot;, 
  &quot;Count on me cecapore PR and new shitizen count as locals ok People no need serve ns Jin songggg&quot;, 
  &quot;muddywaters said Thats in EP holders from INDIA its actually quite scary thought This is likely to creep up to in EP holders from INDIA By then they will be even more 嚣张 Click to expand they already took over mandarin gardens&quot;, 
  &quot;greentiger said The problem with CECAs is that they have almost no supporters here This is not the case with PRCs Click to expand Tiongs faced much worse than ceca back then There were so many public antitiong actions where many even made it to mainstream media Newspapers reporting issues or clashes with tiongs used to be a norm The that ceca faced is considered mild already&quot;, 
  &quot;Handyman said There should not have that gap if your Sinkie grads are educated to what the market needs the skills set that are sought after by employers and not just for statistics reporting that you have a highly educated work force So your gov is just plugging the holes using FT cos it seems like your educated grads are unable to fill that gapnow somewhere must be very wrong for things to turn out this way Click to expand Do you know that your skills set become obsolete faster in this time and age Whose responsibility is it to gain skills Who benefits with the right skills set&quot;, 
  &quot;iamcat said the foreigners applying for jobs back then when i was recruiting were mostly from developing economies with low per capita gdp many of these economies also are not really developed market economies where the work experience is at international level their education certificates many are also from universities where copyingcorruption has been known to occur widely i seldom saw back then resumes from developed economies though there were a few here and there such as japan usa uk europe australia hk Click to expand Who live in comfortable country will want to switch Similarly as mentioned talented hard working and smart Singaporeans would have jobs Only left over sinkies who are toxic in their workplace have no skillset not talented then think that CECA steal their jobs&quot;
  ), date = c(&quot;2021-07-13 00:00:00 UTC&quot;, &quot;2020-09-22 00:00:00 UTC&quot;, 
  &quot;2021-08-07 00:00:00 UTC&quot;, &quot;2021-04-07 00:00:00 UTC&quot;, &quot;2021-07-23 00:00:00 UTC&quot;, 
  &quot;2021-07-06 00:00:00 UTC&quot;, &quot;2021-07-08 00:00:00 UTC&quot;, &quot;2021-07-13 00:00:00 UTC&quot;, 
  &quot;2021-07-06 00:00:00 UTC&quot;, &quot;2021-07-06 00:00:00 UTC&quot;), responsibility_attribution = c(4, 
  3, 1, 2, 4, 1, 2, 4, 1, 4)), row.names = c(NA, -10L), class = c(&quot;tbl_df&quot;, 
  &quot;tbl&quot;, &quot;data.frame&quot;))
  
  </code></pre>
  
Tags: <neural-networks><reinforcement-learning><machine-learning><deep-learning><natural-language-processing>
Title: Using naive bayesian vs. transformer-based architecture model for human-annotated data?
UserDisplayName: maldini1990
ThreadText: # Using naive bayesian vs. transformer-based architecture model for human-annotated data?
  
  (Asked by: username_0 on 2023-12-10)
  
  I have a reddit dataset with thousands of online posts over the economy and inflation. We have used human-annotation on 60% of posts to determine whether users blame the following entities over the current inflation: We coded 1 for posts blaming Democrats, We coded 2 for posts blaming Republicans, We coded 3 for posts blaming the Federal Reserve, We coded 4 for posts that are negative about the economy/inflation but are not particularly blaming any political party or government entity over the economy.
  My goal is to use the human-annotated posts to determine who is being blamed in the remaining 50% of posts that have not been labelled, and I am not sure if using a naive bayes model is appropriate here or if other ML models are more suitable? I have seen some guides like this that recommend using a naive bayesian model for cases like mine but I am not sure if they are suitable?
  #data example
  dput(df[1:10,c(1,2,3,4,8)])
  
  #data output:
  
  structure(list(id = c(1933, 7161, 4661, 2885, 5102, 5052, 4585, 
  1935, 5259, 891), username = c("the_dog", "Empyrean Cobalt", 
  "Engineeer", "AuraKUPO", "kyo_465", "Johnpierpontmorgan", "ToughLuck", 
  "SmallQuietDog", "kyo_465", "LessonsIndissent"), post = c("Think jhk AT Pinoy etc all should be grateful to cecas for taking the heat off them", 
  "Scamdetector said I hope you all realised that UOB actually didnt answer Clement question Click to expand UOB is just using the same template to reply like robotic mode", 
  "netzach said this Daniels guy Click to expand I think people are not understanding WP and Daniel Goh in their entirely", 
  "TS is still a student Companies must pay FT CPF in cash instead Only sinkies need to pay CPF a cut of their pay", 
  "ponpokku said at least they dun downgrade like CECA pose no threats to locals CECA ish u have to double work to kio their sai Click to expand I dun want to hire people who tell me there are those who are worse than them", 
  "Count on me cecapore PR and new shitizen count as locals ok People no need serve ns Jin songggg", 
  "muddywaters said Thats in EP holders from INDIA its actually quite scary thought This is likely to creep up to in EP holders from INDIA By then they will be even more 嚣张 Click to expand they already took over mandarin gardens", 
  "greentiger said The problem with CECAs is that they have almost no supporters here This is not the case with PRCs Click to expand Tiongs faced much worse than ceca back then There were so many public antitiong actions where many even made it to mainstream media Newspapers reporting issues or clashes with tiongs used to be a norm The that ceca faced is considered mild already", 
  "Handyman said There should not have that gap if your Sinkie grads are educated to what the market needs the skills set that are sought after by employers and not just for statistics reporting that you have a highly educated work force So your gov is just plugging the holes using FT cos it seems like your educated grads are unable to fill that gapnow somewhere must be very wrong for things to turn out this way Click to expand Do you know that your skills set become obsolete faster in this time and age Whose responsibility is it to gain skills Who benefits with the right skills set", 
  "iamcat said the foreigners applying for jobs back then when i was recruiting were mostly from developing economies with low per capita gdp many of these economies also are not really developed market economies where the work experience is at international level their education certificates many are also from universities where copyingcorruption has been known to occur widely i seldom saw back then resumes from developed economies though there were a few here and there such as japan usa uk europe australia hk Click to expand Who live in comfortable country will want to switch Similarly as mentioned talented hard working and smart Singaporeans would have jobs Only left over sinkies who are toxic in their workplace have no skillset not talented then think that CECA steal their jobs"
  ), date = c("2021-07-13 00:00:00 UTC", "2020-09-22 00:00:00 UTC", 
  "2021-08-07 00:00:00 UTC", "2021-04-07 00:00:00 UTC", "2021-07-23 00:00:00 UTC", 
  "2021-07-06 00:00:00 UTC", "2021-07-08 00:00:00 UTC", "2021-07-13 00:00:00 UTC", 
  "2021-07-06 00:00:00 UTC", "2021-07-06 00:00:00 UTC"), responsibility_attribution = c(4, 
  3, 1, 2, 4, 1, 2, 4, 1, 4)), row.names = c(NA, -10L), class = c("tbl_df", 
  "tbl", "data.frame"))
  
  **Tags:** neural-networks, reinforcement-learning, machine-learning, deep-learning, natural-language-processing
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-12-11. Score: 0
  
  Given the complexity of your dataset and the potential relationships among words and entities, a transformer-based architecture, such as BERT (Bidirectional Encoder Representations from Transformers), could be a more appropriate choice.
  
  It's worth noting that the selection of the model ultimately depends on various factors, including the size of your dataset, computational resources available, and the desired level of accuracy. It would be beneficial to experiment with different models and evaluate their performance on a validation set before selecting the most suitable one for your task.
  
  
  ---
  
  