Artificial intelligence’s sentiment analysis ability has potential, but certain obstacles slow its progression. This technology’s inability to account for subtle cultural differences in language may be responsible for its low penetration rate. Why do algorithms struggle with culture? Does a solution exist yet?
The Issue With Conventional Sentiment Analysis
A natural language processing (NLP) model’s ability to decipher the attitude or undertone of text-based messages is promising but lacks dependability. Conventional sentiment analysis often misses subtle cues and overlooks emotionally charged phrases because it is incapable of emotional intelligence.
AI often confuses metaphors, sarcasm and hyperbole because it takes things at face value without considering context, causing misclassifications. Since figurative language deviates from ordinary language and isn’t meant to be taken literally by definition, algorithms’ struggle with it is unsurprising, but is that an excuse?
Although NLP technology is well established, it is imperfect. But should companies expect it to be? After all, the average human has trouble recognizing figurative speech and text-based tone. Should models be held to a higher standard? In short, the answer is yes. Those using AI for sentiment analysis should anticipate and address this possibility.
According to an IBM study, about 16% of companies used NLP-driven sentiment analysis in 2022 to improve customer and employee care. Since this technology is still in its adoption phase, addressing misclassification issues early on is crucial for widespread acceptance and long-term success.
While advanced sentiment analysis tools can offer valuable insights into the emotions of messages, their accuracy is ultimately dependent on their training dataset. Decision-makers who hope to use this technology for marketing, conflict resolution or staff retention measurements must consider whether they developed their model with diversity in mind.
Why Cultural Differences Can Lead to Misclassification
Emotions, tones and expressions aren’t universal – regional societal norms and local dialects shape them. While people living in Western countries tend to value individuality and expressiveness, many in collectivist countries prioritize community, self-sacrifice and indirectness. These differences often manifest in speech.
Two people from different backgrounds could mean the same thing but phrase it entirely differently – and vice versa. For example, greeting someone by candidly remarking on their appearance may indicate affection in one culture and be considered rude in another.
Context is an important factor since various cultures view frankness and negativity differently. For example, people in Japan often avoid direct refusals, opting for apologies or implied disapproval instead. In this case, one person’s thinly veiled attempt to soften their criticism with sarcasm may come off as a seemingly obvious joke to people from the United States.
An NLP model designed for a Western audience may classify the phrase, “That meeting was so much fun. I enjoyed sitting on a call for two hours instead of getting an email about the report,” as positive, even though the speaker has sarcastic intent.
Navigating cultural nuances is challenging enough for worldly adults. A context-blind AI lacking emotional intelligence and critical reasoning needs to be handheld to begin to understand the subtle differences in tone, intent and language that develop because of variations in local, regional and societal norms.
Tips for Conducting Cross-Cultural Sentiment Analysis
Brands attempting to conduct a cross-culture sentiment analysis should consider these tips.
1. Use Emotional Detection
Since standard sentiment analysis detects polarity – positivity, neutrality or negativity – instead of the underlying emotions being communicated, it produces less precise descriptions of textual data. On the other hand, emotional detection can identify more complex feelings expressed in text by considering word choice and sentiment strength.
2. Leverage Diverse Datasets
Unfortunately, like all algorithms, NLP models often inadvertently develop biases. Not accounting for language-based cultural differences during sentiment analysis will likely lead to prejudice, stereotyping or misclassifications. Diversifying datasets to encompass multiple cultures can mitigate this issue.
3. Develop an Uncertainty System
Forcing an NLP model to classify every statement is counterintuitive because its overconfidence may skew output accuracy. AI engineers should create an uncertainty mechanism, allowing it to flag phrases it has trouble with. Having a human in the loop to deal with these notifications is more manageable than fixing misclassifications.
4. Opt for a Multimodal Analysis
Since unstructured information accounts for up to 90% of the data enterprises collect, they spend much of their time cleaning and transforming – after which the dataset‘s original intent may be lost in translation. A multimodal sentiment analysis considers visual, language and audio instead of text alone, increasing its accuracy.
5. Don’t Translate the Text
Translating text to a common language like English before conducting a sentiment analysis is an unwise decision because it may skew findings. Instead, decision-makers should develop a multilingual model and analyze the content in its language of origin. This way, they reduce mistranslations and miscommunications, improving output precision.
Important Considerations for Model Selection
A rule-based model is often one of the most simplistic. It applies a predefined ruleset to text to identify specific words or phrases connected to certain emotions. It’s useful when businesses only need to get the gist of the message or have a human-in-the-loop system to analyze the output for consistency.
A deep learning model is ideal for processing incomplete or informal phrases. People often use incorrect grammar, improper phrasing and inadequate punctuation when writing reviews or using internal messaging apps – the mediums many companies use to conduct their sentiment analysis.
Research shows a dual-model approach can help AI understand complex speech. It requires one model explicitly trained for sentiment analysis and the other for figurative language – sarcasm, in this case – to improve stand-alone sentiment classification. The algorithm achieved an 80% accuracy rating and outperformed its counterpart by 5.49%.
Consider Culture During Sentiment Analysis
Cultural differences affect how people speak and what they mean, making them fundamental to any sentiment analysis. Even if a United States-based brand is training a model exclusively for American customers, it should still consider language in the broader context of culture. An AI able to detect these subtleties will outperform other algorithms and achieve higher accuracy.
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