I just got off the phone with a researcher this morning who is interested in looking at sentiment analysis on a corpus of fiction, specifically by having some native speakers of Japanese (I think) tag adjectives as positive or negative, then look at the overall shape of the corpus with those tags in mind.
A while back, I wrote a paper about geoparsing and sentiment analysis for a class, describing a project I worked on. Talking to this researcher made me think back to this project – which I’m actually currently trying to rewrite in Python and then make work on some Japanese, rather than Victorian English, texts – and my own definition of sentiment analysis for humanistic inquiry.*
How is my definition of sentiment analysis different? How about I start with the methodology? What I did was look for salient adjectives, which I searched for by looking at most “salient” nouns (not necessarily the most frequent, but I need to refine my heuristics) and then the adjectives that appeared next to them. I also used Wordnet to look for words related to these adjectives and nouns to expand my search beyond just those specific words to ones with similar meaning that I might have missed (in particular, I looked at hypernyms (broader terms) and synonyms of nouns, and synonyms of adjectives).
My method of sentiment analysis ends up looking more like automatic summarization than a positive-negative sentiment analysis we more frequently encounter, even in humanistic work such as Matt Jockers’s recent research. I argue, of course, that my method is somewhat more meaningful. I consider all adjectives to be sentiment words, because they carry subjective judgment (even something that’s kind of green might be described by someone else as also kind of blue). And I’m more interested in the character of subjective judgment than whether it should be able to be considered ‘objectively’ as positive or negative (something I don’t think is really possible in humanistic inquiry, and even in business applications). In other words, if we have to pick out the most representative feelings of people about what they’re experiencing, what are they feeling about that experience?
After all, can you really say that weather is good or bad, that there being a lot of farm fields is good or bad? I looked at 19th-century British women’s travel narratives of “exotic” places, and I found that their sentiment was often just observations about trains and the landscape and the people. They didn’t talk about whether they were feeling positively or negatively about those things; rather, they gave us their subjective judgment of what those things were like.
My take on sentiment analysis, then, is clearly that we need to introduce human judgment to the end of the process, perhaps gathering these representative phrases and adjectives (I lean toward phrases or even whole sentences) and then deciding what we can about them. I don’t even think a human interlocutor could put down a verdict of positive or negative on these observations and judgments – sentiments – that the women had about their experiences and environments. If not even a human could do it, and humans write and train the algorithms, how can the computer do it?
Is there even a point? Does it matter if it’s possible or not? We should be looking for something else entirely.
(I really need to get cracking on this project. Stay tuned for the revised methodology and heuristics, because I hope to write more and share code here as I go along.)
* I’m also trying to write a more extensive and revised paper on this, meant for the new incarnation of LLC.