Exploring Japanese Sentiment Analysis with oseti Package

Sentiment analysis, also known as opinion mining, is a technique used to determine the sentiment or emotion expressed in a piece of text. While sentiment analysis has been widely studied for English text, analyzing sentiment in other languages poses unique challenges. In this article, we will focus on sentiment analysis for the Japanese language using the oseti package.

The oseti package is a dictionary-based sentiment analysis tool specifically designed for Japanese text. It leverages two evaluation polarity dictionaries, one for verbs and another for nouns, to analyze sentiment in Japanese sentences. These dictionaries were created based on extensive research and provide a comprehensive set of evaluative expressions for sentiment analysis.

To get started with the oseti package, we first need to install it. We can easily do this using pip:

$ pip install oseti

Once the package is installed, we can import the oseti module and create an instance of the Analyzer class:

import oseti

analyzer = oseti.Analyzer()

Now, let’s see how the oseti package can perform sentiment analysis on Japanese text. We can use the analyze method to get sentiment scores for a given sentence:

sentence = '天国で待ってる。'
result = analyzer.analyze(sentence)
print(result)
# Output: [1.0]

In this example, the sentence “天国で待ってる。” is evaluated as having a sentiment score of 1.0, indicating a positive sentiment.

We can also count the number of positive and negative expressions in a sentence using the count_polarity method:

sentence = '遅刻したけど楽しかったし嬉しかった。すごく充実した！'
result = analyzer.count_polarity(sentence)
print(result)
# Output: [{'positive': 2, 'negative': 1}, {'positive': 1, 'negative': 0}]

In this case, the sentence contains two positive expressions and one negative expression.

The oseti package also provides a detailed analysis of sentiment expressions in a sentence using the analyze_detail method:

sentence = 'お金も希望もない！'
result = analyzer.analyze_detail(sentence)
print(result)
# Output: [{'positive': [], 'negative': ['お金-NEGATION', '希望-NEGATION'], 'score': -1.0}]

Here, the detailed analysis reveals that the sentence contains negative expressions for “お金” and “希望” with a sentiment score of -1.0.

In addition to the default dictionaries, we can also apply our own custom dictionaries for sentiment analysis. This can be useful when analyzing domain-specific content or when we want to include additional evaluative expressions. We can provide custom word dictionaries using the word_dict parameter and custom wago dictionaries using the wago_dict parameter:

custom_word_dict = {'カワイイ': 'p', 'ブサイク': 'n'}
custom_wago_dict = {'イカ する': 'ポジ', 'まがまがしい': 'ネガ'}

analyzer = oseti.Analyzer(word_dict=custom_word_dict, wago_dict=custom_wago_dict)

Now, let’s analyze some sentences using the custom dictionaries:

sentence = 'カワイイ'
result = analyzer.analyze_detail(sentence)
print(result)
# Output: [{'positive': ['カワイイ'], 'negative': [], 'score': 1.0}]

sentence = 'ブサイクだ'
result = analyzer.analyze_detail(sentence)
print(result)
# Output: [{'positive': [], 'negative': ['ブサイク'], 'score': -1.0}]

sentence = 'イカすよ'
result = analyzer.analyze_detail(sentence)
print(result)
# Output: [{'positive': ['イカ する'], 'negative': [], 'score': 1.0}]

In these examples, we can see how the custom dictionaries influence the sentiment analysis results.

In conclusion, the oseti package provides a powerful tool for sentiment analysis in the Japanese language. By leveraging carefully curated dictionaries, we can accurately analyze the sentiment expressed in Japanese text. Furthermore, the ability to apply custom dictionaries allows for more specialized sentiment analysis in specific domains or contexts.

Category: Natural Language Processing

Tags: sentiment analysis, Japanese language, oseti, dictionary-based, custom dictionaries