Why (Traditional) Sentiment Analysis Sucks
Mar 7, 2023
Everywhere I go, people are asking me, “Hey Austin, why does traditional sentiment analysis suck?
If I’m being honest, that’s never happened… but I do look forward to that day. I’m so prepared in fact, I’ll talk about it for an hour, then link them to this article. Always be kind to your future self.
Traditional (Negative, Neutral, Positive) sentiment analysis, also known as rule-based sentiment analysis, uses a predefined set of rules to analyze text and determine whether it expresses a positive, negative, or neutral sentiment. This approach relies on a lexicon or dictionary of words and phrases that are associated with specific sentiments, such as happy or sad.
The lexicon consists of a list of words and their corresponding polarity scores, indicating whether the word is associated with positive or negative sentiment. For example, the word "love" may be assigned a positive score, while the word "hate" may be assigned a negative score. The polarity score for each word is based on its common usage and context, as determined by human experts.
The sentiment analysis algorithm breaks down the input text into individual words and phrases and looks up their polarity scores in the lexicon. The algorithm then calculates the overall sentiment score for the text by aggregating the polarity scores of the individual words and phrases. TL;DR, the math you learned in 2nd grade.
There are different methods for aggregating polarity scores, such as averaging or summing, depending on the specific sentiment analysis algorithm. Once the sentiment score is calculated, the algorithm classifies the text as positive, negative, or neutral based on a predefined threshold. For example, if the sentiment score is above a certain value, the text is classified as positive.
While traditional sentiment analysis is relatively simple and easy to implement (although Marketing teams sure make it sound snazzy), it has several huge limitations. First, it relies on a static lexicon that does not take into account the context, language, or cultural differences of the text being analyzed. NYC people are different than Mobile AL, who are different than Mexico City, and Madrid. To be effective, you can’t treat everyone the same.
Second, it does not capture the nuances of human languages, such as sarcasm (my love language BTW), irony, or humor.
Third, it cannot detect the emotional and cognitive state of the writer or speaker, which can significantly affect the sentiment expressed in the text.
This is where more modern approaches to sentiment analysis, such as machine learning and deep learning, (or quantum-deep-neutral network-crypto-web3) methods come into play. These approaches use algorithms that can learn from data and adapt to different contexts, languages, and emotional states.
As a result, they can provide more accurate and nuanced sentiment analysis, making them increasingly popular in applications such as customer service, social media monitoring, and market research.
So there you have it - why is traditional sentiment analysis so bad? Because it’s a glorified keyword lookup tied to point values and counted up. Now go throw stuff into our demo app on our homepage and watch it work its AI magic!
-Austin
Everywhere I go, people are asking me, “Hey Austin, why does traditional sentiment analysis suck?
If I’m being honest, that’s never happened… but I do look forward to that day. I’m so prepared in fact, I’ll talk about it for an hour, then link them to this article. Always be kind to your future self.
Traditional (Negative, Neutral, Positive) sentiment analysis, also known as rule-based sentiment analysis, uses a predefined set of rules to analyze text and determine whether it expresses a positive, negative, or neutral sentiment. This approach relies on a lexicon or dictionary of words and phrases that are associated with specific sentiments, such as happy or sad.
The lexicon consists of a list of words and their corresponding polarity scores, indicating whether the word is associated with positive or negative sentiment. For example, the word "love" may be assigned a positive score, while the word "hate" may be assigned a negative score. The polarity score for each word is based on its common usage and context, as determined by human experts.
The sentiment analysis algorithm breaks down the input text into individual words and phrases and looks up their polarity scores in the lexicon. The algorithm then calculates the overall sentiment score for the text by aggregating the polarity scores of the individual words and phrases. TL;DR, the math you learned in 2nd grade.
There are different methods for aggregating polarity scores, such as averaging or summing, depending on the specific sentiment analysis algorithm. Once the sentiment score is calculated, the algorithm classifies the text as positive, negative, or neutral based on a predefined threshold. For example, if the sentiment score is above a certain value, the text is classified as positive.
While traditional sentiment analysis is relatively simple and easy to implement (although Marketing teams sure make it sound snazzy), it has several huge limitations. First, it relies on a static lexicon that does not take into account the context, language, or cultural differences of the text being analyzed. NYC people are different than Mobile AL, who are different than Mexico City, and Madrid. To be effective, you can’t treat everyone the same.
Second, it does not capture the nuances of human languages, such as sarcasm (my love language BTW), irony, or humor.
Third, it cannot detect the emotional and cognitive state of the writer or speaker, which can significantly affect the sentiment expressed in the text.
This is where more modern approaches to sentiment analysis, such as machine learning and deep learning, (or quantum-deep-neutral network-crypto-web3) methods come into play. These approaches use algorithms that can learn from data and adapt to different contexts, languages, and emotional states.
As a result, they can provide more accurate and nuanced sentiment analysis, making them increasingly popular in applications such as customer service, social media monitoring, and market research.
So there you have it - why is traditional sentiment analysis so bad? Because it’s a glorified keyword lookup tied to point values and counted up. Now go throw stuff into our demo app on our homepage and watch it work its AI magic!
-Austin
Everywhere I go, people are asking me, “Hey Austin, why does traditional sentiment analysis suck?
If I’m being honest, that’s never happened… but I do look forward to that day. I’m so prepared in fact, I’ll talk about it for an hour, then link them to this article. Always be kind to your future self.
Traditional (Negative, Neutral, Positive) sentiment analysis, also known as rule-based sentiment analysis, uses a predefined set of rules to analyze text and determine whether it expresses a positive, negative, or neutral sentiment. This approach relies on a lexicon or dictionary of words and phrases that are associated with specific sentiments, such as happy or sad.
The lexicon consists of a list of words and their corresponding polarity scores, indicating whether the word is associated with positive or negative sentiment. For example, the word "love" may be assigned a positive score, while the word "hate" may be assigned a negative score. The polarity score for each word is based on its common usage and context, as determined by human experts.
The sentiment analysis algorithm breaks down the input text into individual words and phrases and looks up their polarity scores in the lexicon. The algorithm then calculates the overall sentiment score for the text by aggregating the polarity scores of the individual words and phrases. TL;DR, the math you learned in 2nd grade.
There are different methods for aggregating polarity scores, such as averaging or summing, depending on the specific sentiment analysis algorithm. Once the sentiment score is calculated, the algorithm classifies the text as positive, negative, or neutral based on a predefined threshold. For example, if the sentiment score is above a certain value, the text is classified as positive.
While traditional sentiment analysis is relatively simple and easy to implement (although Marketing teams sure make it sound snazzy), it has several huge limitations. First, it relies on a static lexicon that does not take into account the context, language, or cultural differences of the text being analyzed. NYC people are different than Mobile AL, who are different than Mexico City, and Madrid. To be effective, you can’t treat everyone the same.
Second, it does not capture the nuances of human languages, such as sarcasm (my love language BTW), irony, or humor.
Third, it cannot detect the emotional and cognitive state of the writer or speaker, which can significantly affect the sentiment expressed in the text.
This is where more modern approaches to sentiment analysis, such as machine learning and deep learning, (or quantum-deep-neutral network-crypto-web3) methods come into play. These approaches use algorithms that can learn from data and adapt to different contexts, languages, and emotional states.
As a result, they can provide more accurate and nuanced sentiment analysis, making them increasingly popular in applications such as customer service, social media monitoring, and market research.
So there you have it - why is traditional sentiment analysis so bad? Because it’s a glorified keyword lookup tied to point values and counted up. Now go throw stuff into our demo app on our homepage and watch it work its AI magic!
-Austin