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  1. PO
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    <p>What you intend to accomplish is quite complex (mainly delivering an acceptable accuracy) and there is no simple solution.</p> <p>Basic ideas of the approach I would take in your position:</p> <ol> <li>It can exclusively be used for a given language (e.g., English), the one you consider while performing the development. Relying on translations to bring other languages into account would affect its reliability a lot. A new "starting analysis" would be required for each new language you want to consider. </li> <li>The most important part of this development will be the database and thus you would have to put a big focus on its design, connectivity, data storage/retrieval, etc.</li> <li>Minimum content for the database: a list of words, their associated "value" (timid, angry, etc.), their importance within the given value (the f word getting a 8 out of 10 for "aggressive"), how likely is this word to have a different meaning (as suggested in the comment above, "sick" might get a warning flag to check the exact context), further considerations (e.g., how the number of repetitions affects its value), etc. Additionally, you would have to include different levels of storage (per post, per user, per time, etc.).</li> <li>You have to create an algorithm adaptable and scalable enough (lots of changes, improvements, additions, etc. are expected here) to deliver what you want. The basic idea I would come up from is: assessing each post on account of the values for each word as defined in the database (by considering each word alone, the number of repetitions of the given word, the context of the given word, etc.), that is, checking which words are analysis-worthy and which ones are not; a parsing system not only capable to extract individual words but to analyse the context (words before and after the target one in the same sentence/paragraph or in a different one); setting up some rules to avoid "misunderstandings" (e.g., minimum number of posts to consider that a behaviour is aggressive, otherwise just ignore (perhaps just humour); accounting for complex moods formed on account of different types of posts (e.g., angry in 3 posts + timid in 9 posts = *); etc.); in summary, it has to be capable of converting the "discrete posts reality" into the desired output (an assessment for the post/user) as accurately as possible.</li> </ol> <p>As said, it will not be easy. But if you do things step by step and make sure that the structure is adaptable enough to allow any modification/extension, you might get a pretty reliable piece of software (by understanding what is the ideal result you can expect, that is, a ranking of the type of language used; extrapolating this to a real personality analysis sounds perhaps too ambitious) which might be of interest to quite a few people.</p>
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    1. POC# - Personality Analysis based on on Word/Language
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    1. USvarocarbas
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    1. COhey thanks. But how should i go about developing the algorithm? Kinda new to this.
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      1. USSamuel Lee
    2. CO@SamuelLee This is not the kind of development for a newie. You should start with something simpler. Or, if you want to start with this project, you should focus on solving very specific problems (for example: simple parsing system capable of extracting a word and putting it into the context given by all the surrounding ones). In a 0-10 scale of complexity for data-analysis/management program development (including intrinsic difficulty of the algorithm and DB optimisation to account for the expected amount of data), delivering an accurate-enough solution for this might easily get an 8.
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    3. COHm. what do you mean by 8?
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      1. USSamuel Lee
 

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